diff --git "a/data/s4e4.jsonl" "b/data/s4e4.jsonl" new file mode 100644--- /dev/null +++ "b/data/s4e4.jsonl" @@ -0,0 +1,934 @@ +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1638192504568697} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.14812928594757882} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n epsilon = np.finfo(float).eps\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14918930478676834} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__reg_alpha': [0.1, 1.0, 10.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regression_model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regression_model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.14953162904499348} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n INTERACTION_FEATURES=['Shell_weight', 'Diameter', 'Length', 'Whole_weight', 'Shucked_weight']\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n epsilon = np.finfo(float).eps\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n for col_name in config.INTERACTION_FEATURES:\n if col_name in df_fe.columns:\n if f'Sex_I' in df_fe.columns:\n df_fe[f'Sex_I_x_{col_name}'] = df_fe['Sex_I'] * df_fe[col_name]\n if f'Sex_M' in df_fe.columns:\n df_fe[f'Sex_M_x_{col_name}'] = df_fe['Sex_M'] * df_fe[col_name]\n if f'Sex_F' in df_fe.columns:\n df_fe[f'Sex_F_x_{col_name}'] = df_fe['Sex_F'] * df_fe[col_name]\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14920258045312} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str # one of the named scorers in sklearn.metrics.\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n# Custom RMSLE scorer function\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n \"\"\"\n Calculates the Root Mean Squared Logarithmic Error (RMSLE).\n Ensures predictions are non-negative before applying log1p.\n \"\"\"\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n # Ensure predictions are non-negative, as log1p is used.\n # The target variable `Rings` is always positive (min 1).\n y_pred = np.maximum(y_pred, 0)\n # Handle cases where y_true or y_pred could have NaNs if not preprocessed,\n # though for this competition, y_true should be clean.\n # mean_squared_log_error returns np.inf if 0 is present for y_true or y_pred\n # and squared=False for y_pred <= -1. We already ensured y_pred >= 0.\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Defines all constants and configuration flags for the script.\n \"\"\"\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n POLYNOMIAL_DEGREE=4,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Loads training and test data, separates the target, preserves test IDs,\n and performs initial data cleaning and column alignment.\n \"\"\"\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n # Separate target variable\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n # Drop rows with NaN in the target variable\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n # Preserve test IDs before dropping the ID column from x_test\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n # Drop ID and target columns from features\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n # Handle Height=0 values by converting them to NaN.\n # A SimpleImputer will handle these NaNs later in the preprocessing pipeline.\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n # Check for non-numeric types before replacement if necessary, though it should be numeric.\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n # Align columns between x_train and x_test after initial drops.\n # This is crucial for consistent feature sets for the preprocessor and model.\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n # Add columns missing in test_df, fill with a placeholder (e.g., 0 or np.nan)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 # Consistent with handling of new categorical features by OneHotEncoder\n\n # Remove columns present in test_df but not in train_df (should ideally not happen with this dataset)\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n # Ensure x_test columns are in the same order as x_train columns\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Defines and returns a ColumnTransformer for data preprocessing,\n including robust scaling, polynomial features, and one-hot encoding.\n \"\"\"\n x_train = data.x_train\n\n # Dynamically identify numerical and categorical features\n # 'Sex' is typically 'object' or 'category' dtype\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n # Remove 'Sex' from numerical features list if it was accidentally included (e.g., if encoded as numbers already)\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n # Numerical feature pipeline: Impute NaNs, generate Polynomial Features, then Robust Scale.\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')), # Handles NaNs (e.g., from Height=0)\n ('poly', PolynomialFeatures(degree=config.POLYNOMIAL_DEGREE, include_bias=False)), # Generate extensive polynomial and interaction features\n ('scaler', RobustScaler()) # Apply Robust Scaling as requested, which is good for outliers\n ])\n\n # Categorical feature pipeline: Impute (if any NaNs), then One-Hot Encode.\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')), # Handles potential NaNs in 'Sex'\n ('onehot', OneHotEncoder(handle_unknown='ignore')) # One-hot encode 'Sex'\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' # Keep any other columns not specified (e.g., if there were unexpected dtypes)\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Defines and returns an instantiated Scikit-Learn compatible Ridge regression model,\n wrapped with TransformedTargetRegressor to handle the RMSLE metric.\n \"\"\"\n # Use TransformedTargetRegressor to optimize for RMSLE indirectly.\n # It applies np.log1p to the target before fitting the regressor\n # and np.expm1 to the predictions before returning them.\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p, # Apply log1p to y_train for the base regressor\n inverse_func=np.expm1 # Apply expm1 to predictions of the base regressor\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n \"\"\"\n Specifies a hyperparameter grid for GridSearchCV for the Ridge model.\n \"\"\"\n # Hyperparameter grid for Ridge (alpha) with 'model__regressor__' prefix for Pipeline compatibility.\n # The range is chosen to explore different regularization strengths for the Ridge model.\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0] # 4 values, well within limits\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n \"\"\"\n Provides the scoring metric function (RMSLE) for GridSearchCV.\n \"\"\"\n # Use make_scorer to convert the custom rmsle_scorer function into a Scikit-learn compatible scorer.\n # greater_is_better=False because RMSLE is an error metric (lower is better).\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n \"\"\"\n Defines and returns an instantiated cross-validation splitter.\n KFold is used for regression tasks.\n \"\"\"\n # KFold cross-validation for regression, shuffled for randomness, with a fixed random_state for reproducibility.\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Creates the submission DataFrame using the model's predictions on the test set.\n Applies necessary post-processing steps to the predictions.\n \"\"\"\n # Predict on the test set using the best estimator from GridSearchCV.\n # The TransformedTargetRegressor in the pipeline will inverse-transform predictions automatically.\n predictions = model.predict(data.x_test)\n\n # Post-processing: Clip predictions to the valid range of 'Rings' (1 to 29).\n # This prevents physically implausible predictions and helps align with target bounds.\n # Forum insights suggest clipping but not rounding to integer for RMSLE.\n predictions = np.clip(predictions, 1, 29)\n\n # Create the submission DataFrame in the required format.\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15235558515952802} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489900466550185} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489900466550185} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.14894626808369082} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489900466550185} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.1503392542694313} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__reg_alpha': [0.1, 1.0, 10.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regression_model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regression_model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.1503392542694313} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__reg_alpha': [0.1, 1.0, 10.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regression_model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regression_model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.1503392542694313} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634024615937277} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan \n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n TRANSFORM_TARGET_LOG1P=True, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16464534730089495} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634024615937277} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan \n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n TRANSFORM_TARGET_LOG1P=True, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dataset = load_data(cfg)\n prep = get_preprocessor(cfg, dataset)\n m = get_model(cfg)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', prep),\n ('model', m)\n ])\n full_pipeline.fit(dataset.x_train, dataset.y_train)\n sub = get_submission(full_pipeline, dataset, cfg)\n sub.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634024615937277} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n if 'Height' in data.x_train.columns:\n data.x_train.loc[data.x_train['Height'] == 0, 'Height'] = np.nan\n if 'Height' in data.x_test.columns:\n data.x_test.loc[data.x_test['Height'] == 0, 'Height'] = np.nan\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14906103261619333} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634024615937277} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dataset = load_data(cfg)\n prep = get_preprocessor(cfg, dataset)\n m = get_model(cfg)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', prep),\n ('model', m)\n ])\n full_pipeline.fit(dataset.x_train, dataset.y_train)\n sub = get_submission(full_pipeline, dataset, cfg)\n sub.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dataset = load_data(cfg)\n prep = get_preprocessor(cfg, dataset)\n m = get_model(cfg)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', prep),\n ('model', m)\n ])\n full_pipeline.fit(dataset.x_train, dataset.y_train)\n sub = get_submission(full_pipeline, dataset, cfg)\n sub.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n if 'Height' in data.x_train.columns:\n data.x_train.loc[data.x_train['Height'] == 0, 'Height'] = np.nan\n if 'Height' in data.x_test.columns:\n data.x_test.loc[data.x_test['Height'] == 0, 'Height'] = np.nan\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14906103261619333} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column {config.TARGET_COLUMN} not found in train data.\")\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} not found in test data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n for col in data.x_train.columns:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n if not hasattr(data, 'x_train'):\n raise AttributeError(\"data object must have x_train attribute\")\n\n numerical_features = data.x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = data.x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 1.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object must have x_test attribute\")\n if not hasattr(data, 'test_ids'):\n raise AttributeError(\"data object must have test_ids attribute\")\n\n predictions = model.predict(data.x_test)\n\n # Target 'Rings' must be positive for MSLE; clip to a reasonable biological range\n predictions = np.clip(predictions, 1, 30)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14884559701938946} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan \n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', '\u96fb\u8ecameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.18284564784044893} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan \n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.18284564784044893} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan \n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n TRANSFORM_TARGET_LOG1P=True, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df[config.TARGET_COLUMN].copy() \n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = train_df.drop(columns=[config.ID_COLUMN, config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.18284564784044893} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan \n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.18284564784044893} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\nclass StackingRegressor(BaseEstimator, RegressorMixin):\n def __init__(self, base_models: List[Model], meta_model: Model, n_splits: int = 5, random_state: int = 42):\n if not isinstance(base_models, list) or not all(isinstance(m, BaseEstimator) for m in base_models):\n raise TypeError(\"base_models must be a list of scikit-learn estimators.\")\n if not isinstance(meta_model, BaseEstimator):\n raise TypeError(\"meta_model must be a scikit-learn estimator.\")\n if not isinstance(n_splits, int) or n_splits < 2:\n raise ValueError(\"n_splits must be an integer >= 2.\")\n if not isinstance(random_state, int):\n raise ValueError(\"random_state must be an integer.\")\n\n self.base_models = base_models\n self.meta_model = meta_model\n self.n_splits = n_splits\n self.random_state = random_state\n self.fitted_base_models_full_data_ = None\n self.meta_model_ = None\n\n def fit(self, X: pd.DataFrame, y: pd.Series):\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if not isinstance(y, (pd.Series, np.ndarray)):\n raise TypeError(\"y must be a pandas Series or numpy array.\")\n if X.shape[0] != y.shape[0]:\n raise ValueError(\"X and y must have the same number of samples.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n y_arr = y.to_numpy() if isinstance(y, pd.Series) else y\n\n n_samples = X_arr.shape[0]\n n_base_models = len(self.base_models)\n\n oof_predictions = np.zeros((n_samples, n_base_models))\n kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state)\n\n for i, (train_idx, val_idx) in enumerate(kf.split(X_arr, y_arr)):\n X_train_fold, y_train_fold = X_arr[train_idx], y_arr[train_idx]\n X_val_fold = X_arr[val_idx]\n\n for j, base_model_orig in enumerate(self.base_models):\n model = clone(base_model_orig)\n model.fit(X_train_fold, y_train_fold)\n oof_predictions[val_idx, j] = model.predict(X_val_fold)\n\n self.meta_model_ = clone(self.meta_model)\n self.meta_model_.fit(oof_predictions, y_arr)\n\n self.fitted_base_models_full_data_ = []\n for base_model_orig in self.base_models:\n model = clone(base_model_orig)\n model.fit(X_arr, y_arr)\n self.fitted_base_models_full_data_.append(model)\n\n return self\n\n def predict(self, X: pd.DataFrame) -> np.ndarray:\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if self.fitted_base_models_full_data_ is None or self.meta_model_ is None:\n raise RuntimeError(\"StackingRegressor not fitted. Call fit() first.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n n_test_samples = X_arr.shape[0]\n n_base_models = len(self.fitted_base_models_full_data_)\n\n base_test_predictions = np.zeros((n_test_samples, n_base_models))\n for j, model in enumerate(self.fitted_base_models_full_data_):\n base_test_predictions[:, j] = model.predict(X_arr)\n\n final_predictions = self.meta_model_.predict(base_test_predictions)\n final_predictions = np.maximum(0, final_predictions)\n\n return final_predictions\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n xgbr = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n base_models = [lgbm, xgbr, hgbm]\n meta_model = Ridge(random_state=config.RANDOM_STATE)\n\n stacking_regressor = StackingRegressor(\n base_models=base_models,\n meta_model=meta_model,\n n_splits=config.N_STACKING_FOLDS,\n random_state=config.RANDOM_STATE\n )\n return stacking_regressor\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__meta_model__alpha': [0.1, 1.0, 10.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n stacking_model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', stacking_model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.14956252429587946} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import TransformedTargetRegressor\n\n# Protocols for type safety, as provided in the problem description\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, # Corrected typo here\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\n# Type alias for any valid scikit-learn CV splitter instance\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str # one of the named scorers in sklearn.metrics.\n\n@runtime_checkable\nclass Scorer(Protocol):\n \"\"\"\n A protocol for any callable object that returns a float score.\n \"\"\"\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Defines all constants and configuration flags for the script.\n \"\"\"\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], # No specific columns to drop beyond ID/Target for this problem's instructions\n TARGET_MIN=1,\n TARGET_MAX=29,\n # Post-processing rule: predictions >= NUDGE_THRESHOLD_LOW and < TARGET_MAX are nudged to TARGET_MAX\n NUDGE_THRESHOLD_LOW=27.5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Loads training and test data, performs basic cleaning, separates target,\n preserves test IDs, and aligns columns.\n \"\"\"\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n # Data Cleaning: Replace physically impossible 'Height' values of 0 with NaN\n # GBDT models can handle NaNs, and imputers in preprocessor will fill them.\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n # Robustly drop ID and target columns from training data\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n # Preserve test IDs BEFORE dropping the ID column from x_test\n data.test_ids = test_df[config.ID_COLUMN].copy()\n # Robustly drop ID column from test data\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n # Align columns between x_train and x_test to ensure consistency\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n # Add columns missing in test_df (if any) and fill with 0 (or np.nan for numerical)\n # For this problem, usually columns are consistent, but this is robust.\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan # Consistent with numerical imputer\n else:\n data.x_test[c] = 'missing' # Placeholder for categorical\n\n # Drop columns present in test_df but not in train_df (should not happen if train_df has all features)\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n # Ensure x_test columns are in the same order as x_train\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Defines and returns a ColumnTransformer for data preprocessing.\n Dynamically identifies numerical and categorical features.\n \"\"\"\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), # Fill NaNs with median\n ('scaler', StandardScaler()) # Scale numerical features\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), # Fill NaNs with most frequent\n ('onehot', OneHotEncoder(handle_unknown='ignore')) # One-hot encode for models\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' # Keep any other columns, though not expected after setup\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Defines and returns an instantiated, Scikit-Learn compatible LGBMRegressor model.\n Utilizes TransformedTargetRegressor to handle the RMSLE objective through log1p transformation.\n \"\"\"\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2', # Optimize for Mean Squared Error on the log-transformed target\n random_state=config.RANDOM_STATE,\n n_jobs=-1, # Use all available cores\n # Other parameters will be tuned by GridSearchCV\n )\n\n # TransformedTargetRegressor handles the log1p transformation for training\n # and expm1 for inverse transformation of predictions automatically.\n model = TransformedTargetRegressor(\n regressor=lgbm_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n \"\"\"\n Specifies a hyperparameter grid for GridSearchCV for the LGBMRegressor.\n The grid is kept compact to stay within recommended limits.\n \"\"\"\n # The 'model__' prefix is required for parameters of the model within the Pipeline.\n # For TransformedTargetRegressor, parameters of the inner regressor are accessed via 'model__regressor__'.\n # Example grid with 48 total parameter combinations (2*2*3*2*2)\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7], # -1 means no limit\n 'model__regressor__reg_alpha': [0.1, 1.0], # L1 regularization\n 'model__regressor__reg_lambda': [0.1, 1.0], # L2 regularization\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n \"\"\"\n Provides a custom scorer function for Root Mean Squared Logarithmic Error (RMSLE).\n Ensures predictions are non-negative before applying the logarithmic transform.\n \"\"\"\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n # RMSLE requires non-negative inputs.\n # Target values (y_true) are already >= 1 per problem description.\n # Predictions (y_pred) should be clipped to a minimum of 0 (or config.TARGET_MIN)\n # to prevent log(negative) errors, although np.expm1 generally yields non-negative.\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n # make_scorer ensures it integrates correctly with GridSearchCV.\n # greater_is_better=False because we want to minimize RMSLE.\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n \"\"\"\n Defines and returns an instantiated cross-validation splitter (KFold).\n \"\"\"\n # KFold is a standard choice for regression problems.\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Generates predictions on the test set, applies post-processing,\n and creates the submission DataFrame.\n \"\"\"\n # Generate raw predictions from the best model\n predictions = model.predict(data.x_test)\n\n # Post-processing Step 1: Clipping predictions to the known target range [1, 29]\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n # Post-processing Step 2: Rule-based adjustment - nudging predictions near 28 towards 29\n # This rule applies to predictions that fall within [NUDGE_THRESHOLD_LOW, TARGET_MAX)\n # and sets them to TARGET_MAX (29).\n predictions = np.where(\n (predictions >= config.NUDGE_THRESHOLD_LOW) & (predictions < config.TARGET_MAX),\n config.TARGET_MAX,\n predictions\n )\n\n # Create the submission DataFrame in the required format\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14779170349733076} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n df_fe = combined_df.copy() \n\n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}. This should not happen if FE is applied consistently.\")\n\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\n# Type aliases for scikit-learn compatibility\nCVSplitter = Union[KFold, StratifiedKFold] # Simplified for common use\nModel = BaseEstimator # General model type\nProcessor = sklearn.pipeline.Pipeline # Preprocessor type\nScorerString = str # Scorer string type\n\n\n# Custom RMSLE metric function\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n \"\"\"Calculates Root Mean Squared Logarithmic Error (RMSLE).\"\"\"\n # Ensure predictions are non-negative before log transformation\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\nclass StackingRegressor(BaseEstimator, RegressorMixin):\n \"\"\"\n A custom two-layer stacking regressor.\n Level 1: Diverse base models (LGBM, XGB, HGBM).\n Level 2: Ridge meta-regressor.\n \"\"\"\n def __init__(self, base_models: List[Model], meta_model: Model, n_splits: int = 5, random_state: int = 42):\n if not isinstance(base_models, list) or not all(isinstance(m, BaseEstimator) for m in base_models):\n raise TypeError(\"base_models must be a list of scikit-learn estimators.\")\n if not isinstance(meta_model, BaseEstimator):\n raise TypeError(\"meta_model must be a scikit-learn estimator.\")\n if not isinstance(n_splits, int) or n_splits < 2:\n raise ValueError(\"n_splits must be an integer >= 2.\")\n if not isinstance(random_state, int):\n raise ValueError(\"random_state must be an integer.\")\n\n self.base_models = base_models\n self.meta_model = meta_model\n self.n_splits = n_splits\n self.random_state = random_state\n\n # Attributes to be set during fitting\n self.fitted_base_models_full_data_ = None\n self.meta_model_ = None\n\n def fit(self, X: pd.DataFrame, y: pd.Series):\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if not isinstance(y, (pd.Series, np.ndarray)):\n raise TypeError(\"y must be a pandas Series or numpy array.\")\n if X.shape[0] != y.shape[0]:\n raise ValueError(\"X and y must have the same number of samples.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n y_arr = y.to_numpy() if isinstance(y, pd.Series) else y\n\n n_samples = X_arr.shape[0]\n n_base_models = len(self.base_models)\n\n oof_predictions = np.zeros((n_samples, n_base_models))\n\n # This KFold is for generating OOF predictions for the meta-model\n kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state)\n\n for i, (train_idx, val_idx) in enumerate(kf.split(X_arr, y_arr)):\n X_train_fold, y_train_fold = X_arr[train_idx], y_arr[train_idx]\n X_val_fold = X_arr[val_idx]\n\n for j, base_model_orig in enumerate(self.base_models):\n model = clone(base_model_orig)\n\n # Fit base model on training fold\n model.fit(X_train_fold, y_train_fold)\n\n # Store OOF predictions\n oof_predictions[val_idx, j] = model.predict(X_val_fold)\n\n # Train meta-model on OOF predictions\n self.meta_model_ = clone(self.meta_model)\n self.meta_model_.fit(oof_predictions, y_arr)\n\n # Train base models on the full dataset for final test predictions\n self.fitted_base_models_full_data_ = []\n for base_model_orig in self.base_models:\n model = clone(base_model_orig)\n model.fit(X_arr, y_arr)\n self.fitted_base_models_full_data_.append(model)\n\n return self\n\n def predict(self, X: pd.DataFrame) -> np.ndarray:\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if self.fitted_base_models_full_data_ is None or self.meta_model_ is None:\n raise RuntimeError(\"StackingRegressor not fitted. Call fit() first.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n\n n_test_samples = X_arr.shape[0]\n n_base_models = len(self.fitted_base_models_full_data_)\n\n base_test_predictions = np.zeros((n_test_samples, n_base_models))\n for j, model in enumerate(self.fitted_base_models_full_data_):\n base_test_predictions[:, j] = model.predict(X_arr)\n\n final_predictions = self.meta_model_.predict(base_test_predictions)\n\n # Post-processing: predictions for RMSLE should not be negative\n final_predictions = np.maximum(0, final_predictions)\n\n return final_predictions\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, # KFold splits for outer GridSearchCV\n N_STACKING_FOLDS=5, # KFold splits for internal stacking OOF generation\n COLS_TO_DROP=[],\n # List of features for Sex interaction based on analysis\n INTERACTION_FEATURES=['Shell_weight', 'Diameter', 'Length', 'Whole_weight', 'Shucked_weight']\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n # Handle NaN in target by dropping.\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n # Robustly drop columns from training data\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n # Preserve test IDs and robustly drop columns from test data\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n # --- Feature Engineering ---\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n # Replace Height=0 with NaN as it's a physical impossibility and GBDT models handle NaN well.\n # This is specifically recommended in the tabular_analysis.\n # Add check for 'Height' column existence to prevent KeyError\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n # If 'Height' is missing, ensure the column exists for downstream calculations, filled with NaN\n df_fe['Height'] = np.nan\n\n # Use epsilon for other divisions by zero for numerical stability\n epsilon = np.finfo(float).eps\n\n # Physical Ratio Features - Add checks for existence of all involved columns\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n # approx_density\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n # bmi\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n # meat_ratio\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n # shell_ratio\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n # viscera_ratio\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n # length_dia_ratio\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n # length_height_ratio\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n # water_loss\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n # Sex Interaction Features (One-hot encode Sex first)\n if 'Sex' in df_fe.columns:\n # Ensure all possible 'Sex' categories are present in both train and test to avoid missing columns\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n # If 'Sex' column is missing, add placeholder OHE columns as 0 to ensure downstream consistency\n for s_cat in ['I', 'M', 'F']: # Ensure these columns exist for interaction features\n df_fe[f'Sex_{s_cat}'] = 0\n\n # Create interaction terms\n for col_name in config.INTERACTION_FEATURES:\n if col_name in df_fe.columns: # Check if feature exists after potential dropping/NaN handling\n if f'Sex_I' in df_fe.columns:\n df_fe[f'Sex_I_x_{col_name}'] = df_fe['Sex_I'] * df_fe[col_name]\n if f'Sex_M' in df_fe.columns:\n df_fe[f'Sex_M_x_{col_name}'] = df_fe['Sex_M'] * df_fe[col_name]\n if f'Sex_F' in df_fe.columns: # Also add Female for completeness\n df_fe[f'Sex_F_x_{col_name}'] = df_fe['Sex_F'] * df_fe[col_name]\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n # Align columns between train and test after feature engineering\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n # Add missing columns to test_df and fill with 0 (or a suitable default)\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n # Add missing columns to train_df and fill with 0 (should ideally not happen if FE is consistent)\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n # Ensure column order is the same\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n # Dynamically identify numerical features.\n # After FE in load_data, 'Sex' columns are ints, so they are numerical here.\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n # No explicit categorical features expected after one-hot encoding in load_data,\n # but a robust preprocessor would handle them if they appeared.\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), # Median for robustness to outliers and NaN from Height=0\n ('scaler', StandardScaler()) \n ])\n\n # If there are actual categorical features remaining (unlikely after manual OHE)\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n # Base Models with RMSLE objective handling and reasonable default parameters\n # LGBM and HistGBM use TransformedTargetRegressor for log1p transformation\n # XGBoost uses its native reg:squaredlogerror objective\n\n # LightGBM Regressor with log1p transformation\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, # Increased for better performance\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, # L1 regularization\n reg_lambda=0.1, # L2 regularization\n colsample_bytree=0.7, # Feature subsampling\n subsample=0.7, # Bagging fraction\n n_jobs=-1,\n verbose=-1, # Suppress verbose output\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n # XGBoost Regressor with native squared log error objective\n xgbr = xgb.XGBRegressor(\n objective='reg:squaredlogerror', # Directly optimizes RMSLE\n random_state=config.RANDOM_STATE,\n n_estimators=150, # Increased\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist', # Use histogram-based algorithm for faster training\n eval_metric='rmsle' \n )\n\n # HistGradientBoostingRegressor with log1p transformation\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150, # n_estimators equivalent\n learning_rate=0.04,\n max_leaf_nodes=25, # num_leaves equivalent\n max_depth=7,\n l2_regularization=0.1, # L2 regularization\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n base_models = [lgbm, xgbr, hgbm]\n\n # Meta-regressor\n meta_model = Ridge(random_state=config.RANDOM_STATE)\n\n # Instantiate the custom stacking regressor\n stacking_regressor = StackingRegressor(\n base_models=base_models,\n meta_model=meta_model,\n n_splits=config.N_STACKING_FOLDS, # KFold splits for OOF generation\n random_state=config.RANDOM_STATE\n )\n return stacking_regressor\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n # Hyperparameter grid for the StackingRegressor\n # We tune the meta_model's alpha parameter\n return ParameterGrid({\n 'model__meta_model__alpha': [0.1, 1.0, 10.0], # Tuning alpha for the Ridge meta-regressor\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n # Scikit-learn's make_scorer for custom RMSLE function\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n # For the outer GridSearchCV, using KFold.\n # While StratifiedKFold on binned targets can be beneficial for regression,\n # implementing it here would require pre-binning y which is not directly\n # passed to get_cv_splitter in the harness. KFold is a robust general choice.\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n # The model (which is our StackingRegressor inside the Pipeline)\n # has already been fitted by GridSearchCV.best_estimator_\n\n predictions = model.predict(data.x_test)\n\n # Post-processing: Clipping predictions to the known range (1 to 29)\n # This is a common and effective step as advised in the deep_research_report\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n # Do NOT round to integer, as advised in the deep_research_report\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14972714769859793} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Defines all constants and configuration flags for the script.\n \"\"\"\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Loads, cleans, and feature engineers the data.\n \"\"\"\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n sub_weight_cols = [\"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"]\n if all(col in combined_df.columns for col in sub_weight_cols + [\"Whole_weight\"]):\n temp_sum_sub_weights = combined_df[sub_weight_cols].fillna(0).sum(axis=1)\n mask_whole_weight_too_small = combined_df['Whole_weight'] < temp_sum_sub_weights\n combined_df.loc[mask_whole_weight_too_small, 'Whole_weight'] = \\\n temp_sum_sub_weights.loc[mask_whole_weight_too_small]\n for sub_col in sub_weight_cols:\n mask_sub_weight_too_large = combined_df[sub_col] > combined_df['Whole_weight']\n combined_df.loc[mask_sub_weight_too_large, sub_col] = \\\n combined_df.loc[mask_sub_weight_too_large, 'Whole_weight']\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Defines and returns a ColumnTransformer for data preprocessing.\n \"\"\"\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Defines and returns an instantiated, Scikit-Learn compatible LightGBM model.\n \"\"\"\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV.\n \"\"\"\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n \"\"\"\n Provide the scoring metric.\n \"\"\"\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n \"\"\"\n Define and return an instantiated cross-validation splitter.\n \"\"\"\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Creates the submission DataFrame.\n \"\"\"\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.feature_selection import RFE\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n N_FEATURES_TO_SELECT=20,\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n model = RFE(\n estimator=lgbm_estimator,\n n_features_to_select=config.N_FEATURES_TO_SELECT,\n step=0.1,\n verbose=0\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_features_to_select': [15, 25, 35],\n 'model__step': [0.1, 0.2],\n 'model__estimator__n_estimators': [200, 400],\n 'model__estimator__learning_rate': [0.01, 0.05],\n 'model__estimator__num_leaves': [20, 31],\n 'model__estimator__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n# Type aliases and protocols from the prompt for type safety\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n # Other sklearn CV splitters like RepeatedKFold, StratifiedKFold, etc., can be added if needed\n]\n\nScorerString = str # one of the named scorers in sklearn.metrics.\n\n@runtime_checkable\nclass Scorer(Protocol):\n \"\"\"\n A protocol for any callable object that returns a float score.\n \"\"\"\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Defines all constants and configuration flags for the script.\n \"\"\"\n config = types.SimpleNamespace()\n\n # File paths and columns\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n # Reproducibility\n config.RANDOM_STATE = 42\n\n # Cross-validation\n config.N_SPLITS = 5\n\n # Data cleaning specific configurations\n # List of physical measurement columns that cannot be zero or negative\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n # A small positive value used for temporary filling during feature engineering\n # to avoid division by zero/NaN, ensuring features become NaN for invalid inputs.\n config.FE_TEMP_MIN_VAL = 1e-6 \n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Loads, cleans, and feature engineers the data.\n \"\"\"\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n # Preserve test IDs before dropping the ID column\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Separate target variable from training features\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) # Apply log1p transformation for RMSLE\n\n # Drop ID and target columns from training features\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n # Drop ID column from test features\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n # Combine for consistent cleaning and feature engineering\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n # --- Comprehensive Outlier and Zero-Value Cleaning ---\n\n # 1. Replace 0s and negative values in physical measurements with NaN\n # Abalones cannot have zero or negative length, diameter, height, or any weight.\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n # Replace 0s with NaN\n combined_df[col] = combined_df[col].replace(0, np.nan)\n # Replace negative values with NaN\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n # 2. Detect and correct inconsistent Whole_weight vs. sub-weights\n sub_weight_cols = [\"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"]\n if all(col in combined_df.columns for col in sub_weight_cols + [\"Whole_weight\"]):\n # Calculate sum of sub-weights (handling NaNs by treating them as 0 for the sum check,\n # but the original NaNs will propagate to Water_Loss later if not imputed)\n temp_sum_sub_weights = combined_df[sub_weight_cols].fillna(0).sum(axis=1)\n\n # Correction 1: Ensure Whole_weight is at least the sum of sub-weights\n # If Whole_weight is less than sum_sub_weights, it's inconsistent.\n # We assume Whole_weight should be >= sum_sub_weights.\n mask_whole_weight_too_small = combined_df['Whole_weight'] < temp_sum_sub_weights\n\n # Adjust Whole_weight to be at least the sum of sub-weights where it's inconsistent.\n # This prevents negative Water_Loss due to Whole_weight being too small.\n combined_df.loc[mask_whole_weight_too_small, 'Whole_weight'] = \\\n temp_sum_sub_weights.loc[mask_whole_weight_too_small]\n\n # Correction 2: Ensure individual sub-weights do not exceed Whole_weight\n # This is important if Whole_weight was adjusted upwards, or if sub-weights were initially erroneous.\n for sub_col in sub_weight_cols:\n mask_sub_weight_too_large = combined_df[sub_col] > combined_df['Whole_weight']\n combined_df.loc[mask_sub_weight_too_large, sub_col] = \\\n combined_df.loc[mask_sub_weight_too_large, 'Whole_weight'] # Cap sub_weight at Whole_weight\n\n # --- Domain-specific physical feature engineering ---\n\n df_fe = combined_df.copy() # Work on a copy for FE\n\n # Create temporary columns for feature engineering calculations to handle NaNs gracefully.\n # Replace NaNs with a small positive value temporarily for calculations,\n # so that divisions don't error out, but invalid results become NaN.\n cols_for_fe_temp = [col for col in config.PHYSICAL_COLS if col in df_fe.columns]\n for col in cols_for_fe_temp:\n df_fe[f'{col}_FE_temp'] = df_fe[col].fillna(config.FE_TEMP_MIN_VAL)\n\n # Helper function to safely get a temp feature or return None\n def get_fe_col(col_name: str) -> pd.Series | None:\n temp_col_name = f'{col_name}_FE_temp'\n return df_fe[temp_col_name] if temp_col_name in df_fe.columns else None\n\n length = get_fe_col('Length')\n diameter = get_fe_col('Diameter')\n height = get_fe_col('Height')\n whole_weight = get_fe_col('Whole_weight')\n shucked_weight = get_fe_col('Shucked_weight')\n viscera_weight = get_fe_col('Viscera_weight')\n shell_weight = get_fe_col('Shell_weight')\n\n # Top_Surface_Area\n if length is not None and diameter is not None:\n df_fe['Top_Surface_Area'] = length * diameter\n else:\n df_fe['Top_Surface_Area'] = np.nan\n\n # Abalone_Density (Whole_weight / (Length * Diameter * Height))\n # If any denominator part was originally NaN/0, the product will be small and density large.\n # The fillna(config.FE_TEMP_MIN_VAL) handles this: if original was NaN, it's a small value, so density will be large.\n # To ensure features correctly reflect original NaNs from 0s in denom:\n # If original `Length`, `Diameter`, or `Height` was NaN, `Abalone_Density` should also be NaN.\n if whole_weight is not None and length is not None and diameter is not None and height is not None:\n # Only compute if original components were not NaN\n mask = combined_df['Length'].notna() & combined_df['Diameter'].notna() & combined_df['Height'].notna() & combined_df['Whole_weight'].notna()\n df_fe['Abalone_Density'] = np.where(mask, whole_weight / (length * diameter * height), np.nan)\n else:\n df_fe['Abalone_Density'] = np.nan\n\n # BMI (Whole_weight / (Height^2))\n if whole_weight is not None and height is not None:\n mask = combined_df['Whole_weight'].notna() & combined_df['Height'].notna()\n df_fe['BMI'] = np.where(mask, whole_weight / (height**2), np.nan)\n else:\n df_fe['BMI'] = np.nan\n\n # Water_Loss (Whole_weight - Shucked_weight - Viscera_weight - Shell_weight)\n if all(x is not None for x in [whole_weight, shucked_weight, viscera_weight, shell_weight]):\n # Ensure Water_Loss is NaN if any component was NaN in original `combined_df`\n mask = combined_df['Whole_weight'].notna() & combined_df['Shucked_weight'].notna() \\\n & combined_df['Viscera_weight'].notna() & combined_df['Shell_weight'].notna()\n df_fe['Water_Loss'] = np.where(mask, whole_weight - shucked_weight - viscera_weight - shell_weight, np.nan)\n else:\n df_fe['Water_Loss'] = np.nan\n # After prior corrections, Water_Loss should be >= 0 if all components were present.\n # If it's still negative due to floating point inaccuracies or complex interaction, cap at 0.\n df_fe['Water_Loss'] = df_fe['Water_Loss'].apply(lambda x: np.maximum(x, 0.0) if pd.notna(x) else np.nan)\n\n\n # Other useful ratio features\n if shucked_weight is not None and whole_weight is not None:\n mask = combined_df['Shucked_weight'].notna() & combined_df['Whole_weight'].notna()\n df_fe['Shucked_Ratio'] = np.where(mask, shucked_weight / whole_weight, np.nan)\n if viscera_weight is not None and whole_weight is not None:\n mask = combined_df['Viscera_weight'].notna() & combined_df['Whole_weight'].notna()\n df_fe['Viscera_Ratio'] = np.where(mask, viscera_weight / whole_weight, np.nan)\n if shell_weight is not None and whole_weight is not None:\n mask = combined_df['Shell_weight'].notna() & combined_df['Whole_weight'].notna()\n df_fe['Shell_Ratio'] = np.where(mask, shell_weight / whole_weight, np.nan)\n if length is not None and diameter is not None:\n mask = combined_df['Length'].notna() & combined_df['Diameter'].notna()\n df_fe['Length_Dia_Ratio'] = np.where(mask, length / diameter, np.nan)\n if length is not None and height is not None:\n mask = combined_df['Length'].notna() & combined_df['Height'].notna()\n df_fe['Length_Height_Ratio'] = np.where(mask, length / height, np.nan)\n if diameter is not None and height is not None:\n mask = combined_df['Diameter'].notna() & combined_df['Height'].notna()\n df_fe['Dia_Height_Ratio'] = np.where(mask, diameter / height, np.nan)\n\n if whole_weight is not None and length is not None and diameter is not None and height is not None:\n mask = combined_df['Whole_weight'].notna() & combined_df['Length'].notna() \\\n & combined_df['Diameter'].notna() & combined_df['Height'].notna()\n df_fe['Total_Weight_Div_Sum_Dim'] = np.where(mask, whole_weight / (length + diameter + height), np.nan)\n\n # Drop the temporary _FE_temp columns\n df_fe = df_fe.drop(columns=[col for col in df_fe.columns if col.endswith('_FE_temp')])\n\n # Split back into x_train and x_test\n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n\n # Align columns: x_test to match x_train (essential after FE, if any columns were dropped/added based on conditions)\n # Ensure x_train and x_test have exactly the same columns in the same order.\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan # Add missing columns to test set with NaN\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}. This should not happen if FE is applied consistently.\")\n\n data.x_test = data.x_test[train_cols] # Reorder test columns to match train\n\n # Explicitly convert 'Sex' column to 'category' dtype if it exists, for potential use by models\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Defines and returns a ColumnTransformer for data preprocessing.\n \"\"\"\n x_train = data.x_train\n\n # Dynamically identify numerical and categorical features\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n # Pipeline for numerical features: Imputation + Scaling\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), # Median for NaNs (from 0s, inconsistencies, derived features)\n ('scaler', sklearn.preprocessing.StandardScaler()) # Standard scaling\n ])\n\n # Pipeline for categorical features: Imputation + One-Hot Encoding\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), # Most frequent for any missing categories\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) # One-hot encode Sex\n ])\n\n # Create a ColumnTransformer to apply different transformers to different columns\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' # Keep other columns (if any) as they are. Should be none in this setup.\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Defines and returns an instantiated, Scikit-Learn compatible LightGBM model.\n \"\"\"\n # Using 'regression' objective (L2 loss) as the target is log1p transformed.\n # This aligns with optimizing for RMSE on the log-transformed target, which corresponds to RMSLE.\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n # Fixed parameters (others will be tuned via get_param_grid)\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV.\n \"\"\"\n # Grid is designed to stay within the recommended 10-100 total combinations.\n # (2 * 2 * 3 * 3 = 36 combinations)\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], # -1 means no limit\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n \"\"\"\n Provide the scoring metric.\n \"\"\"\n # The competition metric is RMSLE (Root Mean Squared Logarithmic Error).\n # GridSearchCV's 'scoring' parameter expects a score to be maximized.\n # 'neg_mean_squared_log_error' returns the negative of MSLE (Mean Squared Logarithmic Error).\n # Maximizing negative MSLE is equivalent to minimizing MSLE.\n # The harness will compute the square root from the stored MSLE to get RMSLE.\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n \"\"\"\n Define and return an instantiated cross-validation splitter.\n \"\"\"\n # KFold with shuffling is generally preferred for this regression task based on competition insights,\n # rather than StratifiedKFold on the regression target.\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Creates the submission DataFrame.\n \"\"\"\n # Predict on the preprocessed test data (which the pipeline handles)\n log_predictions = model.predict(data.x_test)\n\n # Inverse transform the predictions from log1p scale to original scale\n predictions = np.expm1(log_predictions)\n\n # Post-processing:\n # 1. Clip predictions to the valid range for 'Rings' [1, 29]\n # 'Rings' are integer counts typically observed between 1 and 29 in the original dataset.\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n # Do NOT round predictions to integer, as advised by competition insights for RMSLE\n # (rounding can worsen the RMSLE score). Predictions should remain float.\n\n # Create submission DataFrame\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14757261549644857} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n df_fe = combined_df.copy() \n\n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}. This should not happen if FE is applied consistently.\")\n\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, List, Union, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n train_path = pathlib.Path(\"train.csv\")\n test_path = pathlib.Path(\"test.csv\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 12],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(0, y_pred)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.maximum(1, predictions)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14954350561403895} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, List, Union, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n train_path = pathlib.Path(\"train.csv\")\n test_path = pathlib.Path(\"test.csv\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 12],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(0, y_pred)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.maximum(1, predictions)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14802354595414877} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1476466239009968} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n# Protocols for type safety (as provided in the instructions)\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n# Type alias for any valid scikit-learn CV splitter instance\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str # one of the named scorers in sklearn.metrics.\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Define ALL constants and configuration flags for the script in this namespace.\n \"\"\"\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n # No specific columns to drop other than ID and TARGET for this plan.\n COLS_TO_DROP=[], \n # Clipping predictions to the known range of 'Rings'\n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Load data, separate target, drop specified columns, handle simple data issues,\n and align train/test columns. Apply np.log1p transformation to the target.\n \"\"\"\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n # Store test IDs before dropping the ID column\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # CRITICAL: Ensure target does not contain NaNs before transformation\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n # Ensure target is non-negative before log1p transformation\n if (train_df[config.TARGET_COLUMN] < 0).any():\n raise ValueError(\"Target column contains negative values, cannot apply log1p transformation.\")\n\n # Apply target transformation: np.log1p\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n # Robustly drop columns from training data\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n # Robustly drop columns from test data\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n # Handle 'Height' = 0 issue: replace with NaN for imputation\n # This is a data quality fix, not complex FE, allowed by general robustness.\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n # Align columns: Ensure x_test has the same columns as x_train in the same order\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n # Add missing columns to test set, filled with a default value (e.g., 0 or np.nan)\n # Imputer will handle NaNs later. For categorical, 0 might need adjustment.\n # For this dataset, new columns are numerical by nature or will be one-hot encoded.\n data.x_test[c] = 0 \n\n # Drop columns in x_test that are not in x_train (if any, though should be handled by initial drops)\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n # Reorder test columns to match train columns\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Define and return a ColumnTransformer for data preprocessing.\n Uses RobustScaler for numerical features and OneHotEncoder for categorical features.\n \"\"\"\n x_train = data.x_train\n\n # Dynamically identify numerical and categorical features\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n # Preprocessing for numerical features\n # Impute missing values with the median, then scale using RobustScaler\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n # Preprocessing for categorical features\n # Impute missing values with the most frequent value, then One-Hot Encode\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n # Create a ColumnTransformer to apply different transformations to different columns\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' # Keep other columns (if any)\n )\n\n # Optional check to ensure all features are covered if 'remainder'='drop' was used,\n # but with 'passthrough' it's more about ensuring the chosen features are correct.\n # For this simple setup, no complex checks are strictly necessary.\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Define and return an instantiated RandomForestRegressor model.\n \"\"\"\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV for RandomForestRegressor.\n The grid is kept small to adhere to performance constraints.\n \"\"\"\n # Example grid for RandomForestRegressor (2*2*2 = 8 combinations)\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric.\n Since the target (data.y_train) is log1p transformed, mean_squared_error on\n the transformed target is equivalent to RMSLE on the original target.\n We use 'neg_mean_squared_error' because GridSearchCV maximizes the score.\n \"\"\"\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n \"\"\"\n Define and return an instantiated cross-validation splitter (KFold for regression).\n \"\"\"\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n Applies inverse transformation (np.expm1) and clipping to predictions.\n \"\"\"\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n # Make predictions on the preprocessed test data (model expects transformed features)\n predictions_log = model.predict(data.x_test)\n\n # Inverse transform the predictions from log scale to original scale\n predictions = np.expm1(predictions_log)\n\n # Post-processing: Clip predictions to the valid range (1 to 29)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n # Create the submission DataFrame\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n # Ensure the target column is of numeric type\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14861493812306936} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, List, Union, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n train_path = pathlib.Path(\"train.csv\")\n test_path = pathlib.Path(\"test.csv\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 12],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(0, y_pred)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.maximum(1, predictions)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14954350561403895} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg_obj = get_config()\n if cfg_obj.INPUT_DIR.exists():\n data_obj = load_data(cfg_obj)\n preprocessor_obj = get_preprocessor(cfg_obj, data_obj)\n model_instance = get_model(cfg_obj)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor_obj),\n ('model', model_instance)\n ])\n full_pipeline.fit(data_obj.x_train, data_obj.y_train)\n submission_df = get_submission(full_pipeline, data_obj, cfg_obj)\n submission_df.to_csv('submission.csv', index=False)", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"preprocessor\", preprocessor), (\"model\", model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n print(submission.head())", "y": 0.14655067429382065} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, List, Union, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n train_path = pathlib.Path(\"train.csv\")\n test_path = pathlib.Path(\"test.csv\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 12],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(0, y_pred)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.maximum(1, predictions)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14813983224541882} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"preprocessor\", preprocessor), (\"model\", model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n print(submission.head())", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.feature_selection import RFE\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan \n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n N_FEATURES_TO_SELECT=5, \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n TRANSFORM_TARGET_LOG1P=True, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n model = RFE(\n estimator=lgbm_estimator,\n n_features_to_select=config.N_FEATURES_TO_SELECT, \n step=0.1, \n verbose=0 \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_features_to_select': [3, 5, 7], \n 'model__step': [0.1, 0.2], \n 'model__estimator__n_estimators': [200, 400],\n 'model__estimator__learning_rate': [0.01, 0.05],\n 'model__estimator__num_leaves': [20, 31],\n 'model__estimator__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649494233122812} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n df_fe = combined_df.copy() \n\n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}. This should not happen if FE is applied consistently.\")\n\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"preprocessor\", preprocessor), (\"model\", model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n print(submission.head())", "y": 0.14759149323051432} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, List, Union, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n train_path = pathlib.Path(\"train.csv\")\n test_path = pathlib.Path(\"test.csv\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 12],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(0, y_pred)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.maximum(1, predictions)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14802354595414877} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dataset = load_data(cfg)\n prep = get_preprocessor(cfg, dataset)\n m = get_model(cfg)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', prep),\n ('model', m)\n ])\n full_pipeline.fit(dataset.x_train, dataset.y_train)\n sub = get_submission(full_pipeline, dataset, cfg)\n sub.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dataset = load_data(cfg)\n prep = get_preprocessor(cfg, dataset)\n m = get_model(cfg)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', prep),\n ('model', m)\n ])\n full_pipeline.fit(dataset.x_train, dataset.y_train)\n sub = get_submission(full_pipeline, dataset, cfg)\n sub.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dataset = load_data(cfg)\n prep = get_preprocessor(cfg, dataset)\n m = get_model(cfg)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', prep),\n ('model', m)\n ])\n full_pipeline.fit(dataset.x_train, dataset.y_train)\n sub = get_submission(full_pipeline, dataset, cfg)\n sub.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n sub_weight_cols = [\"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"]\n if all(col in combined_df.columns for col in sub_weight_cols + [\"Whole_weight\"]):\n temp_sum_sub_weights = combined_df[sub_weight_cols].fillna(0).sum(axis=1)\n\n mask_whole_weight_too_small = combined_df['Whole_weight'] < temp_sum_sub_weights\n\n combined_df.loc[mask_whole_weight_too_small, 'Whole_weight'] = \\\n temp_sum_sub_weights.loc[mask_whole_weight_too_small]\n\n for sub_col in sub_weight_cols:\n mask_sub_weight_too_large = combined_df[sub_col] > combined_df['Whole_weight']\n combined_df.loc[mask_sub_weight_too_large, sub_col] = \\\n combined_df.loc[mask_sub_weight_too_large, 'Whole_weight']\n\n df_fe = combined_df.copy() \n\n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}. This should not happen if FE is applied consistently.\")\n\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols_final]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if config.INPUT_DIR.exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n print(submission.head())", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv('submission.csv', index=False)", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, List, Union, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n train_path = pathlib.Path(\"train.csv\")\n test_path = pathlib.Path(\"test.csv\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 12],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(0, y_pred)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.maximum(1, predictions)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14953365673177416} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14655067429382065} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, List, Union, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n # Fallback for local testing or different environment\n train_path = pathlib.Path(\"train.csv\")\n test_path = pathlib.Path(\"test.csv\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Handle NaNs in target\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Preserve IDs\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Feature Engineering\n def engineer_features(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n # Numerical features for Abalone\n if all(col in df.columns for col in ['Length', 'Diameter', 'Height']):\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Shape_Index'] = df['Length'] / (df['Diameter'] + 1e-9)\n\n weight_cols = ['Whole weight', 'Whole weight.1', 'Whole weight.2', 'Shell weight']\n if all(col in df.columns for col in weight_cols):\n df['Meat_Yield'] = df['Whole weight.1'] / (df['Whole weight'] + 1e-9)\n df['Viscera_Ratio'] = df['Whole weight.2'] / (df['Whole weight'] + 1e-9)\n df['Shell_Ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n return df\n\n train_df = engineer_features(train_df)\n test_df = engineer_features(test_df)\n\n # Drop columns\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 12],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(0, y_pred)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n # Post-processing: Rings are positive integers, usually clipped in the range of the dataset\n predictions = np.maximum(1, predictions)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14823782163188087} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n columns_to_check_for_zeros = [\n 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight'\n ]\n for df in [data.x_train, data.x_test]:\n for col in columns_to_check_for_zeros:\n if col in df.columns:\n df[col] = df[col].replace(0, np.nan)\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14773869753087515} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, List, Union, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n train_path = pathlib.Path(\"train.csv\")\n test_path = pathlib.Path(\"test.csv\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 12],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(0, y_pred)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.maximum(1, predictions)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14953365673177416} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n if config_obj.INPUT_DIR.exists():\n dataset = load_data(config_obj)\n proc = get_preprocessor(config_obj, dataset)\n base_estimator = get_model(config_obj)\n main_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', proc),\n ('model', base_estimator)\n ])\n main_pipeline.fit(dataset.x_train, dataset.y_train)\n submission_output = get_submission(main_pipeline, dataset, config_obj)\n submission_output.to_csv(\"submission.csv\", index=False)", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n df_fe = combined_df.copy() \n\n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}. This should not happen if FE is applied consistently.\")\n\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1503392542694313} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14897278783872814} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.14872171293212116} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.14872171293212116} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.15019090795566548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__reg_alpha': [0.1, 1.0, 10.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regression_model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regression_model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.15019090795566548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__reg_alpha': [0.1, 1.0, 10.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regression_model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regression_model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.1503392542694313} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__reg_alpha': [0.1, 1.0, 10.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regression_model = get_model(config)\n\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regression_model)\n ])\n\n full_pipeline.fit(data.x_train, data.y_train)\n submission_df = get_submission(full_pipeline, data, config)\n submission_df.to_csv(\"submission.csv\", index=False)", "y": 0.15019090795566548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n df_fe = combined_df.copy() \n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}.\")\n data.x_test = data.x_test[train_cols] \n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16465192742682772} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# Type alias for any valid scikit-learn CV splitter instance\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n raise FileNotFoundError(f\"Data files not found in {config.INPUT_DIR}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Handle NaNs in target\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n def add_features(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n\n # Robust renaming for competition specific naming conventions (S4E4)\n # Often \"Whole weight.1\" is Shucked weight and \"Whole weight.2\" is Viscera weight\n rename_map = {'Whole weight.1': 'Shucked weight', 'Whole weight.2': 'Viscera weight'}\n df = df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns})\n\n # Feature Engineering with existence checks to prevent KeyErrors\n if all(c in df.columns for c in ['Length', 'Diameter', 'Height']):\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n\n if all(c in df.columns for c in ['Shucked weight', 'Whole weight']):\n df['Weight_Ratio'] = df['Shucked weight'] / (df['Whole weight'] + 1e-9)\n\n if all(c in df.columns for c in ['Shell weight', 'Whole weight']):\n df['Shell_Ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n\n if all(c in df.columns for c in ['Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight']):\n df['Weight_Diff'] = df['Whole weight'] - (df['Shucked weight'] + df['Viscera weight'] + df['Shell weight'])\n\n return df\n\n train_df = add_features(train_df)\n test_df = add_features(test_df)\n\n # Robust column dropping for train\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n # Preserve IDs and drop for test\n if config.ID_COLUMN not in test_df.columns:\n raise KeyError(f\"ID column '{config.ID_COLUMN}' missing from test data\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns: Ensure test has exactly what train has\n for col in data.x_train.columns:\n if col not in data.x_test.columns:\n data.x_test[col] = 0\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n # Use TransformedTargetRegressor to handle the log-transformed nature of RMSLE optimization\n base_model = HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=1000,\n early_stopping=True,\n n_iter_no_change=20,\n validation_fraction=0.1\n )\n return TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.05, 0.1],\n 'model__regressor__max_depth': [10, 20],\n 'model__regressor__l2_regularization': [0.0, 1.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Since we use TransformedTargetRegressor(func=np.log1p), minimizing MSE on log(y+1) \n # is mathematically equivalent to minimizing MSLE on the original y.\n # However, to be safe and explicit, we can use 'neg_mean_squared_log_error' \n # as TransformedTargetRegressor's predict() returns the value in the original space.\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n # Rings must be at least 1; np.expm1 should ensure > -1, but explicit clipping is safer\n predictions = np.clip(predictions, 1.0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14881748047944895} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\n# Type alias for any valid scikit-learn CV splitter instance\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Handle NaNs in target\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Robust column dropping for training features\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n # Preserve test IDs before dropping\n if config.ID_COLUMN not in test_df.columns:\n raise KeyError(f\"ID column {config.ID_COLUMN} not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Robust column dropping for test features\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns in case of feature engineering or mismatches\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=0)\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Feature mismatch between training and test sets after alignment.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n if not hasattr(data, 'x_train'):\n raise AttributeError(\"data object must contain x_train for preprocessing.\")\n\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n # HistGradientBoostingRegressor is robust and high-performing for tabular data\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=200,\n early_stopping=True\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n # 2 * 2 * 2 = 8 combinations\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.1, 0.05],\n 'model__max_depth': [3, 5]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # RMSLE is commonly evaluated using MSLE in sklearn\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"data object missing x_test or test_ids.\")\n\n predictions = model.predict(data.x_test)\n\n # Rings are counts and must be non-negative for valid submission and metric scoring\n predictions = np.clip(predictions, 0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14906202433618046} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists() and (config.INPUT_DIR / config.TEST_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN] \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n df_fe = combined_df.copy() \n\n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}. This should not happen if FE is applied consistently.\")\n\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column {config.TARGET_COLUMN} not found in train data.\")\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n for col in data.x_train.columns:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n if not hasattr(data, 'x_train'):\n raise AttributeError(\"data object must have x_train attribute\")\n numerical_features = data.x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = data.x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 1.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object must have x_test attribute\")\n if not hasattr(data, 'test_ids'):\n raise AttributeError(\"data object must have test_ids attribute\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 30)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14884559701938946} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column {config.TARGET_COLUMN} not found in train data.\")\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} not found in test data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n for col in data.x_train.columns:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n if not hasattr(data, 'x_train'):\n raise AttributeError(\"data object must have x_train attribute\")\n\n numerical_features = data.x_train.select_dtypes(include=[np.number]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 1.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object must have x_test attribute\")\n if not hasattr(data, 'test_ids'):\n raise AttributeError(\"data object must have test_ids attribute\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 30)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15060475810370272} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n df_fe = combined_df.copy() \n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}. This should not happen if FE is applied consistently.\")\n data.x_test = data.x_test[train_cols] \n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n raise FileNotFoundError(f\"Data files not found in {config.INPUT_DIR}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Drop rows where target is NaN\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n # Feature Engineering\n for df in [train_df, test_df]:\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Total_Weight'] = df['Whole weight'] + df['Whole weight.1'] + df['Whole weight.2'] + df['Shell weight']\n\n # Extract IDs and drop columns\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n # Align columns\n data.x_test = data.x_test.reindex(columns=data.x_train.columns).fillna(0)\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=200,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=10\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__max_leaf_nodes': [31, 63]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data must contain x_test for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n # Targets for Abalone Rings must be positive (usually >= 1)\n predictions = np.clip(predictions, 1, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1492507761004691} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, List, Union, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n train_path = pathlib.Path(\"train.csv\")\n test_path = pathlib.Path(\"test.csv\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN in train_df.columns:\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 12],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(0, y_pred)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.maximum(1, predictions)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14813983224541882} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[KFold]\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Fail fast if target column is missing or has NaNs\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Robustly drop columns\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n loss='squared_error',\n max_iter=500,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 1.0],\n 'model__max_bins': [255]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Competition evaluation metric is RMSLE\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object must have x_test attribute.\")\n\n predictions = model.predict(data.x_test)\n\n # Rings must be positive for MSLE and logical validity\n predictions = np.clip(predictions, 1e-6, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487190467229933} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Handle NaNs in target\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n\n # Transformation: Log-transform target for RMSLE optimization\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Feature Engineering and alignment\n for col in data.x_train.columns:\n if col not in data.x_test.columns:\n data.x_test[col] = 0\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n if not hasattr(data, 'x_train'):\n raise AttributeError(\"data object must contain x_train\")\n\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n loss='squared_error',\n max_iter=500,\n early_stopping=True,\n scoring='loss'\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_iter': [200, 400],\n 'model__max_leaf_nodes': [31, 63],\n 'model__l2_regularization': [0.0, 0.1]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Since target was log-transformed, MSE in log space is MSLE.\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"data object must contain x_test and test_ids\")\n\n # Predict in log-space\n log_predictions = model.predict(data.x_test)\n\n # Inverse transform to original space\n predictions = np.expm1(log_predictions)\n\n # Post-processing: ensure non-negative and valid range for Abalone\n predictions = np.clip(predictions, 0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14816264039166266} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport lightgbm as lgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n # Fallback for different environments if necessary, but follow schema\n pass\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Handle NaNs in target\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n # Feature Engineering: Feature engineering for Abalone\n def engineer_features(df):\n df = df.copy()\n # Ratios of weights are often important for Abalone\n # Standard columns: Sex, Length, Diameter, Height, Whole weight, Whole weight.1, Whole weight.2, Whole weight.3\n if 'Whole weight' in df.columns and 'Whole weight.1' in df.columns:\n df['weight_ratio_1'] = df['Whole weight.1'] / (df['Whole weight'] + 1e-9)\n if 'Whole weight' in df.columns and 'Whole weight.2' in df.columns:\n df['weight_ratio_2'] = df['Whole weight.2'] / (df['Whole weight'] + 1e-9)\n if 'Whole weight' in df.columns and 'Whole weight.3' in df.columns:\n df['weight_ratio_3'] = df['Whole weight.3'] / (df['Whole weight'] + 1e-9)\n df['volume'] = df['Length'] * df['Diameter'] * df['Height']\n return df\n\n train_df = engineer_features(train_df)\n test_df = engineer_features(test_df)\n\n # Robustly drop columns\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n cols_to_drop_train = [c for c in cols_to_drop_base if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n # Use LightGBM Regressor for high performance on tabular data\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n # Intelligent numerical range for LightGBM\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__subsample': [0.8, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Metric for S4E4 Abalone was RMSLE\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n # Predictions on x_test\n predictions = model.predict(data.x_test)\n\n # Post-processing: Rings must be positive and are usually treated as integers, \n # but the metric is RMSLE on the raw float output. \n # Ensure no negative predictions to avoid MSLE errors.\n predictions = np.clip(predictions, 0.0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14829458303767934} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = [\"Sex\"]\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN] \n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n df_fe = combined_df.copy() \n data.x_train = df_fe.iloc[:len(train_df)].copy()\n data.x_test = df_fe.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns in test_df not found in train_df after FE: {missing_in_train}.\")\n data.x_test = data.x_test[train_cols] \n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14922752317889088} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols_final]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols_final]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14969006178627817} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.15116633895180587} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan \n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n TRANSFORM_TARGET_LOG1P=True, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train_original = train_df[config.TARGET_COLUMN].copy() \n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = train_df.drop(columns=[config.ID_COLUMN, config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14746230603635158} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.15116633895180587} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1638192504568697} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1638192504568697} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n epsilon = np.finfo(float).eps\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15892148295338462} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[],\n INTERACTION_FEATURES=['Shell_weight', 'Diameter', 'Length', 'Whole_weight', 'Shucked_weight']\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n epsilon = np.finfo(float).eps\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n for col_name in config.INTERACTION_FEATURES:\n if col_name in df_fe.columns:\n if f'Sex_I' in df_fe.columns:\n df_fe[f'Sex_I_x_{col_name}'] = df_fe['Sex_I'] * df_fe[col_name]\n if f'Sex_M' in df_fe.columns:\n df_fe[f'Sex_M_x_{col_name}'] = df_fe['Sex_M'] * df_fe[col_name]\n if f'Sex_F' in df_fe.columns:\n df_fe[f'Sex_F_x_{col_name}'] = df_fe['Sex_F'] * df_fe[col_name]\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15925535466882268} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15907504485311397} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n if 'Height' in data.x_train.columns:\n data.x_train.loc[data.x_train['Height'] == 0, 'Height'] = np.nan\n if 'Height' in data.x_test.columns:\n data.x_test.loc[data.x_test['Height'] == 0, 'Height'] = np.nan\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14906103261619333} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15907504485311397} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15909540392778046} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n return df_fe\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15908832947663784} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634024615937277} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n if 'Height' in data.x_train.columns:\n data.x_train.loc[data.x_train['Height'] == 0, 'Height'] = np.nan\n if 'Height' in data.x_test.columns:\n data.x_test.loc[data.x_test['Height'] == 0, 'Height'] = np.nan\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n \"\"\"\n Provide the scoring metric for GridSearchCV.\n \"\"\"\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions.\n \"\"\"\n predictions = model.predict(data.x_test).flatten()\n\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14906103261619333} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n if 'Height' in data.x_train.columns:\n data.x_train.loc[data.x_train['Height'] == 0, 'Height'] = np.nan\n if 'Height' in data.x_test.columns:\n data.x_test.loc[data.x_test['Height'] == 0, 'Height'] = np.nan\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14906103261619333} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n configuration = get_config()\n try:\n loaded_data = load_data(configuration)\n preprocessor_obj = get_preprocessor(configuration, loaded_data)\n regressor = get_model(configuration)\n pipeline_obj = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor_obj),\n ('model', regressor)\n ])\n pipeline_obj.fit(loaded_data.x_train, loaded_data.y_train)\n submission_output = get_submission(pipeline_obj, loaded_data, configuration)\n submission_output.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan \n else:\n data.x_test[c] = 'missing' \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943076153917081} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan \n else:\n data.x_test[c] = 'missing' \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1494324566751729} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan \n else:\n data.x_test[c] = 'missing' \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1493289369644033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n try:\n data_obj = load_data(config_obj)\n processor_obj = get_preprocessor(config_obj, data_obj)\n regressor_obj = get_model(config_obj)\n pipeline_obj = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', processor_obj),\n ('model', regressor_obj)\n ])\n pipeline_obj.fit(data_obj.x_train, data_obj.y_train)\n final_submission = get_submission(pipeline_obj, data_obj, config_obj)\n print(final_submission.head())\n except Exception as error:\n print(error)", "y": 0.148254507105008} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n FEATURE_GEN_OPS_UNARY=[np.log1p, np.sqrt, np.square],\n FEATURE_GEN_OPS_BINARY=['+', '-', '*', '/'],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n numerical_cols = [col for col in combined_train_df.select_dtypes(include=np.number).columns.tolist() if col != config.TARGET_COLUMN]\n\n def generate_features(df: pd.DataFrame, numerical_features: List[str], config: types.SimpleNamespace) -> pd.DataFrame:\n df_copy = df.copy()\n for col in numerical_features:\n if config.FEATURE_GEN_OPS_UNARY:\n for op in config.FEATURE_GEN_OPS_UNARY:\n if op == np.log1p:\n df_copy[f'{col}_log1p'] = np.log1p(df_copy[col].fillna(0).astype(float))\n elif op == np.sqrt:\n df_copy[f'{col}_sqrt'] = np.sqrt(df_copy[col].fillna(0).astype(float).clip(lower=0))\n elif op == np.square:\n df_copy[f'{col}_square'] = np.square(df_copy[col].astype(float))\n\n for i in range(len(numerical_features)):\n for j in range(i + 1, len(numerical_features)):\n col1 = numerical_features[i]\n col2 = numerical_features[j]\n if config.FEATURE_GEN_OPS_BINARY:\n for op_str in config.FEATURE_GEN_OPS_BINARY:\n try:\n if op_str == '+':\n df_copy[f'{col1}_plus_{col2}'] = df_copy[col1] + df_copy[col2]\n elif op_str == '-':\n df_copy[f'{col1}_minus_{col2}'] = df_copy[col1] - df_copy[col2]\n elif op_str == '*':\n df_copy[f'{col1}_times_{col2}'] = df_copy[col1] * df_copy[col2]\n elif op_str == '/':\n with np.errstate(divide='ignore', invalid='ignore'):\n df_copy[f'{col1}_div_{col2}'] = df_copy[col1] / df_copy[col2].replace(0, np.nan)\n except Exception:\n pass\n return df_copy\n\n combined_train_df = generate_features(combined_train_df, numerical_cols, config)\n test_df = generate_features(test_df, numerical_cols, config)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17694993046940088} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df_no_id, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df_no_id[config.TARGET_COLUMN].copy()\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = train_df_no_id.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import TransformedTargetRegressor\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n model = TransformedTargetRegressor(\n regressor=lgbm_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14779170349733076} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n sub_weight_cols = [\"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"]\n if all(col in combined_df.columns for col in sub_weight_cols + [\"Whole_weight\"]):\n temp_sum_sub_weights = combined_df[sub_weight_cols].fillna(0).sum(axis=1)\n mask_whole_weight_too_small = combined_df['Whole_weight'] < temp_sum_sub_weights\n combined_df.loc[mask_whole_weight_too_small, 'Whole_weight'] = \\\n temp_sum_sub_weights.loc[mask_whole_weight_too_small]\n for sub_col in sub_weight_cols:\n mask_sub_weight_too_large = combined_df[sub_col] > combined_df['Whole_weight']\n combined_df.loc[mask_sub_weight_too_large, sub_col] = \\\n combined_df.loc[mask_sub_weight_too_large, 'Whole_weight']\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df_no_id, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = train_df_no_id[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = train_df_no_id.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n# def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.feature_selection import RFE\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n N_FEATURES_TO_SELECT=20,\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n model = RFE(\n estimator=lgbm_estimator,\n n_features_to_select=config.N_FEATURES_TO_SELECT,\n step=0.1,\n verbose=0\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_features_to_select': [15, 25, 35],\n 'model__step': [0.1, 0.2],\n 'model__estimator__n_estimators': [200, 400],\n 'model__estimator__learning_rate': [0.01, 0.05],\n 'model__estimator__num_leaves': [20, 31],\n 'model__estimator__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n sub_weight_cols = [\"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"]\n if all(col in combined_df.columns for col in sub_weight_cols + [\"Whole_weight\"]):\n temp_sum_sub_weights = combined_df[sub_weight_cols].fillna(0).sum(axis=1)\n mask_whole_weight_too_small = combined_df['Whole_weight'] < temp_sum_sub_weights\n combined_df.loc[mask_whole_weight_too_small, 'Whole_weight'] = \\\n temp_sum_sub_weights.loc[mask_whole_weight_too_small]\n\n for sub_col in sub_weight_cols:\n mask_sub_weight_too_large = combined_df[sub_col] > combined_df['Whole_weight']\n combined_df.loc[mask_sub_weight_too_large, sub_col] = \\\n combined_df.loc[mask_sub_weight_too_large, 'Whole_weight']\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if \"Sex\" in data.x_train.columns:\n data.x_train[\"Sex\"] = data.x_train[\"Sex\"].astype(\"category\")\n data.x_test[\"Sex\"] = data.x_test[\"Sex\"].astype(\"category\")\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=[\"object\", \"category\"]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n (\"imputer\", sklearn.impute.SimpleImputer(strategy=\"median\")),\n (\"scaler\", sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n (\"imputer\", sklearn.impute.SimpleImputer(strategy=\"most_frequent\")),\n (\"onehot\", sklearn.preprocessing.OneHotEncoder(handle_unknown=\"ignore\"))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n (\"num\", numerical_transformer, numerical_features),\n (\"cat\", categorical_transformer, categorical_features)\n ],\n remainder=\"passthrough\"\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective=\"regression\",\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n \"model__n_estimators\": [500, 1000],\n \"model__learning_rate\": [0.01, 0.05],\n \"model__num_leaves\": [20, 31, 50],\n \"model__max_depth\": [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return \"neg_mean_squared_log_error\"\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.148254507105008} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols]\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nconfig = get_config()\ntry:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\nexcept Exception:\n pass", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n try:\n data_obj = load_data(config_obj)\n processor_obj = get_preprocessor(config_obj, data_obj)\n regressor_obj = get_model(config_obj)\n pipeline_obj = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', processor_obj),\n ('model', regressor_obj)\n ])\n pipeline_obj.fit(data_obj.x_train, data_obj.y_train)\n final_submission = get_submission(pipeline_obj, data_obj, config_obj)\n print(final_submission.head())\n except Exception as error:\n print(error)", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import TransformedTargetRegressor\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan \n else:\n data.x_test[c] = 'missing' \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n model = TransformedTargetRegressor(\n regressor=lgbm_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14779170349733076} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n FEATURE_GEN_OPS_UNARY=[np.log1p, np.sqrt, np.square],\n FEATURE_GEN_OPS_BINARY=['+', '-', '*', '/'],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n numerical_cols = [col for col in combined_train_df.select_dtypes(include=np.number).columns.tolist() if col != config.TARGET_COLUMN]\n\n def generate_features(df: pd.DataFrame, numerical_features: List[str], config: types.SimpleNamespace) -> pd.DataFrame:\n df_copy = df.copy()\n for col in numerical_features:\n if config.FEATURE_GEN_OPS_UNARY:\n for op in config.FEATURE_GEN_OPS_UNARY:\n if op == np.log1p:\n df_copy[f'{col}_log1p'] = np.log1p(df_copy[col].fillna(0).astype(float))\n elif op == np.sqrt:\n df_copy[f'{col}_sqrt'] = np.sqrt(df_copy[col].fillna(0).astype(float).clip(lower=0))\n elif op == np.square:\n df_copy[f'{col}_square'] = np.square(df_copy[col].astype(float))\n\n for i in range(len(numerical_features)):\n for j in range(i + 1, len(numerical_features)):\n col1 = numerical_features[i]\n col2 = numerical_features[j]\n if config.FEATURE_GEN_OPS_BINARY:\n for op_str in config.FEATURE_GEN_OPS_BINARY:\n try:\n if op_str == '+':\n df_copy[f'{col1}_plus_{col2}'] = df_copy[col1] + df_copy[col2]\n elif op_str == '-':\n df_copy[f'{col1}_minus_{col2}'] = df_copy[col1] - df_copy[col2]\n elif op_str == '*':\n df_copy[f'{col1}_times_{col2}'] = df_copy[col1] * df_copy[col2]\n elif op_str == '/':\n with np.errstate(divide='ignore', invalid='ignore'):\n df_copy[f'{col1}_div_{col2}'] = df_copy[col1] / df_copy[col2].replace(0, np.nan)\n except Exception:\n pass\n return df_copy\n\n combined_train_df = generate_features(combined_train_df, numerical_cols, config)\n test_df = generate_features(test_df, numerical_cols, config)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14769567909880177} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan \n else:\n data.x_test[c] = 'missing' \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1492573452665279} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nconfig = get_config()\ntry:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\nexcept Exception:\n pass", "y": 0.1494553418245299} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if \"Sex\" in data.x_train.columns:\n data.x_train[\"Sex\"] = data.x_train[\"Sex\"].astype(\"category\")\n data.x_test[\"Sex\"] = data.x_test[\"Sex\"].astype(\"category\")\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=[\"object\", \"category\"]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n (\"imputer\", sklearn.impute.SimpleImputer(strategy=\"median\")),\n (\"scaler\", sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n (\"imputer\", sklearn.impute.SimpleImputer(strategy=\"most_frequent\")),\n (\"onehot\", sklearn.preprocessing.OneHotEncoder(handle_unknown=\"ignore\"))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n (\"num\", numerical_transformer, numerical_features),\n (\"cat\", categorical_transformer, categorical_features)\n ],\n remainder=\"passthrough\"\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective=\"regression\",\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n \"model__n_estimators\": [500, 1000],\n \"model__learning_rate\": [0.01, 0.05],\n \"model__num_leaves\": [20, 31, 50],\n \"model__max_depth\": [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return \"neg_mean_squared_log_error\"\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model_obj = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model_obj)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan \n else:\n data.x_test[c] = 'missing' \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1493289369644033} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n columns_to_check_for_zeros = [\n 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight'\n ]\n for df in [data.x_train, data.x_test]:\n for col in columns_to_check_for_zeros:\n if col in df.columns:\n df[col] = df[col].replace(0, np.nan)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14773869753087515} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan \n else:\n data.x_test[c] = 'missing' \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1492573452665279} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n configuration = get_config()\n try:\n loaded_data = load_data(configuration)\n preprocessor_obj = get_preprocessor(configuration, loaded_data)\n regressor = get_model(configuration)\n pipeline_obj = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor_obj),\n ('model', regressor)\n ])\n pipeline_obj.fit(loaded_data.x_train, loaded_data.y_train)\n submission_output = get_submission(pipeline_obj, loaded_data, configuration)\n submission_output.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14655067429382065} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15907504485311397} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15907504485311397} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15907504485311397} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if \"Sex\" in data.x_train.columns:\n data.x_train[\"Sex\"] = data.x_train[\"Sex\"].astype(\"category\")\n data.x_test[\"Sex\"] = data.x_test[\"Sex\"].astype(\"category\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=[\"object\", \"category\"]).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n (\"imputer\", sklearn.impute.SimpleImputer(strategy=\"median\"))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n (\"imputer\", sklearn.impute.SimpleImputer(strategy=\"most_frequent\")),\n (\"onehot\", sklearn.preprocessing.OneHotEncoder(handle_unknown=\"ignore\"))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n (\"num\", numerical_transformer, numerical_features),\n (\"cat\", categorical_transformer, categorical_features)\n ],\n remainder=\"passthrough\"\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective=\"regression\",\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n \"model__n_estimators\": [500, 1000],\n \"model__learning_rate\": [0.01, 0.05],\n \"model__num_leaves\": [20, 31, 50],\n \"model__max_depth\": [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return \"neg_mean_squared_log_error\"\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_depth': [4, 6, 8],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15907504485311397} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if \"Sex\" in data.x_train.columns:\n data.x_train[\"Sex\"] = data.x_train[\"Sex\"].astype(\"category\")\n data.x_test[\"Sex\"] = data.x_test[\"Sex\"].astype(\"category\")\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=[\"object\", \"category\"]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n (\"imputer\", sklearn.impute.SimpleImputer(strategy=\"median\")),\n (\"scaler\", sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n (\"imputer\", sklearn.impute.SimpleImputer(strategy=\"most_frequent\")),\n (\"onehot\", sklearn.preprocessing.OneHotEncoder(handle_unknown=\"ignore\"))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n (\"num\", numerical_transformer, numerical_features),\n (\"cat\", categorical_transformer, categorical_features)\n ],\n remainder=\"passthrough\"\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective=\"regression\",\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n \"model__n_estimators\": [500, 1000],\n \"model__learning_rate\": [0.01, 0.05],\n \"model__num_leaves\": [20, 31, 50],\n \"model__max_depth\": [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return \"neg_mean_squared_log_error\"\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n columns_to_check_for_zeros = [\n 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight'\n ]\n for df in [data.x_train, data.x_test]:\n for col in columns_to_check_for_zeros:\n if col in df.columns:\n df[col] = df[col].replace(0, np.nan)\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14773869753087515} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformations.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features or robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n try:\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n except Exception as e:\n raise FileNotFoundError(f\"Error loading data: {e}\")\n\n # Remove NaNs from target\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Drop columns robustly\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_leaf_nodes': [31, 63],\n 'model__max_depth': [None, 10]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n \n # Rings cannot be less than 0 for MSLE/RMSLE, and logically usually start at 1\n predictions = np.clip(predictions, 1e-6, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.149239880403596} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply polynomial features.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if config.INPUT_DIR.exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv('submission.csv', index=False)", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformation.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if os.path.exists(config.INPUT_DIR):\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport lightgbm as lgb\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n# --- Required Functions ---\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = [] # No extra columns to drop for now\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory not found: {config.INPUT_DIR}\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found: {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found: {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Validate target exists and handle NaNs\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column {config.TARGET_COLUMN} missing from training data.\")\n\n initial_len = len(train_df)\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n if len(train_df) < initial_len:\n print(f\"Dropped {initial_len - len(train_df)} rows with NaN target values.\")\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n # Store Test IDs before dropping\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} missing from test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Define columns to drop\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Feature Engineering: Abalone-specific log transforms for weights (optional but common)\n # Ensure train and test alignment\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=0)\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Feature columns in train and test do not match.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not numerical_features and not categorical_features:\n raise ValueError(\"No features found in the dataset.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n # LightGBM Regressor is excellent for the Abalone dataset\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n # 2 * 2 * 2 = 8 combinations\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [31, 63],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # The Abalone competition (S4E4) evaluation metric is RMSLE.\n # neg_mean_squared_log_error matches this (maximization goal).\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(\n n_splits=config.N_SPLITS,\n shuffle=True,\n random_state=config.RANDOM_STATE\n )\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n # Rings cannot be negative; RMSLE also requires non-negative values.\n # Standard practice is to clip predictions at a logical minimum (e.g., 1 ring).\n predictions = np.clip(predictions, 0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14807367443519404} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformations.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14778357751251783} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = [\"Sex\"]\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0 \n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14778357751251783} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Fail fast: check for target column\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' missing from train data.\")\n\n # Drop rows with NaN in target\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n \n # Feature Engineering (Abalone Specific)\n for df in [train_df, test_df]:\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Weight_Sum'] = df['Whole weight'] + df['Whole weight.1'] + df['Whole weight.2'] + df['Shell weight']\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n \n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n \n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n data.x_test = data.x_test[data.x_train.columns]\n \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=1000,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_leaf_nodes': [31, 63],\n 'model__max_depth': [None, 10],\n 'model__l2_regularization': [0.0, 1.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Since MSLE requires non-negative inputs and GBTs can occasionally predict \n # negative, GridSearchCV with neg_mean_squared_log_error might fail.\n # However, for the purpose of the schema, we provide the standard competition metric.\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object must have x_test\")\n \n predictions = model.predict(data.x_test)\n \n # Post-processing: Rings must be positive. \n # RMSLE is sensitive to non-positive values.\n predictions = np.clip(predictions, 0.001, None)\n \n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n \n return submission_df", "y": 0.1492285039796375} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = [\"Sex\"]\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.14778357751251783} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols]\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nconfig = get_config()\ntry:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\nexcept Exception:\n pass", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n try:\n data_obj = load_data(config_obj)\n processor_obj = get_preprocessor(config_obj, data_obj)\n regressor_obj = get_model(config_obj)\n pipeline_obj = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', processor_obj),\n ('model', regressor_obj)\n ])\n pipeline_obj.fit(data_obj.x_train, data_obj.y_train)\n final_submission = get_submission(pipeline_obj, data_obj, config_obj)\n print(final_submission.head())\n except Exception as error:\n print(error)", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.15116338795748388} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_raw = model.predict(data.x_test)\n predictions = np.clip(predictions_raw, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df_no_id, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df_no_id[config.TARGET_COLUMN].copy()\n data.x_train = train_df_no_id.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nconfig = get_config()\ntry:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\nexcept Exception:\n pass", "y": 0.14944594363281627} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n try:\n data_obj = load_data(config_obj)\n processor_obj = get_preprocessor(config_obj, data_obj)\n regressor_obj = get_model(config_obj)\n pipeline_obj = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', processor_obj),\n ('model', regressor_obj)\n ])\n pipeline_obj.fit(data_obj.x_train, data_obj.y_train)\n final_submission = get_submission(pipeline_obj, data_obj, config_obj)\n print(final_submission.head())\n except Exception as error:\n print(error)", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols]\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df_no_id, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df_no_id[config.TARGET_COLUMN].copy()\n\n data.x_train = train_df_no_id.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n raw_predictions = model.predict(data.x_test)\n predictions = np.clip(raw_predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df_no_id, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = train_df_no_id[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n\n data.x_train = train_df_no_id.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df_no_id, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df_no_id[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n data.x_train = train_df_no_id.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.1492298511262347} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model_inner = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model_inner)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if config.INPUT_DIR.exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # 1. Handle NaNs in Target\n if config.TARGET_COLUMN in train_df.columns:\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n else:\n raise ValueError(f\"Target column {config.TARGET_COLUMN} not found in training data.\")\n\n # 2. Feature Engineering: Volume\n for df in [train_df, test_df]:\n if all(c in df.columns for c in ['Length', 'Diameter', 'Height']):\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n\n data = types.SimpleNamespace()\n\n # 3. Separate y_train\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n # 4. Extract test IDs before dropping\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # 5. Drop columns robustly\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # 6. Align columns\n for col in data.x_train.columns:\n if col not in data.x_test.columns:\n data.x_test[col] = 0\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n if not hasattr(data, 'x_train') or not isinstance(data.x_train, pd.DataFrame):\n raise AttributeError(\"data.x_train must be a pandas.DataFrame.\")\n\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n early_stopping=True,\n max_iter=1000,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_iter': [100, 300],\n 'model__max_depth': [3, 5, 10],\n 'model__l2_regularization': [0.0, 1.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object is missing x_test.\")\n\n predictions = model.predict(data.x_test)\n\n # Post-processing: Rings must be positive\n predictions = np.clip(predictions, 0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488894857152912} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Define ALL constants and configuration flags for the script in this namespace.\n \"\"\"\n config = types.SimpleNamespace()\n\n # File and Directory Names\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n # Column Names\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n # Reproducibility\n config.RANDOM_STATE = 42\n\n # Cross-validation\n config.N_SPLITS = 5\n\n # Columns to drop (none for this basic feature set)\n config.COLS_TO_DROP = []\n\n # Prediction Clipping (min/max values for Rings based on competition context)\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0 # Max rings observed in original abalone data / competition context.\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Load data, separate target, preserve test IDs, and perform basic feature engineering.\n \"\"\"\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n # Handle NaNs in target variable by dropping corresponding rows\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n # Preserve test IDs BEFORE dropping the ID column from x_test\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Robustly drop columns from training data\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n # Robustly drop columns from test data\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n # --- Basic Feature Engineering & Data Cleaning ---\n # As per tabular_analysis, Height=0 is physically impossible.\n # Replace 0s with NaN so imputer can handle it.\n # Add a small epsilon to avoid division by zero\n epsilon = np.finfo(float).eps\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n # Replace zero height with NaN for imputation\n df['Height'] = df['Height'].replace(0, np.nan)\n\n # Create basic ratio features\n if all(col in df.columns for col in ['Length', 'Diameter', 'Height', 'Whole_weight']):\n df['crab_area'] = df['Length'] * df['Diameter']\n # Ensure Height is not NaN before division, if it is, the result will be NaN and handled by imputer.\n # Add epsilon to denominator to prevent division by zero for non-NaN cases.\n df['approx_density'] = df['Whole_weight'] / (df['crab_area'] * df['Height'].fillna(1.0) + epsilon)\n df['bmi'] = df['Whole_weight'] / (df['Height'].fillna(1.0)**2 + epsilon)\n\n if all(col in df.columns for col in ['Shucked_weight', 'Whole_weight']):\n df['meat_ratio'] = df['Shucked_weight'] / (df['Whole_weight'] + epsilon)\n\n if all(col in df.columns for col in ['Shell_weight', 'Whole_weight']):\n df['shell_ratio'] = df['Shell_weight'] / (df['Whole_weight'] + epsilon)\n\n if all(col in df.columns for col in ['Viscera_weight', 'Whole_weight']):\n df['viscera_ratio'] = df['Viscera_weight'] / (df['Whole_weight'] + epsilon)\n\n if all(col in df.columns for col in ['Length', 'Diameter']):\n df['length_dia_ratio'] = df['Length'] / (df['Diameter'] + epsilon)\n\n if all(col in df.columns for col in ['Length', 'Height']):\n df['length_height_ratio'] = df['Length'] / (df['Height'].fillna(1.0) + epsilon) # fillna(1.0) is for ratio calc, imputer handles true NaNs later\n\n if all(col in df.columns for col in ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']):\n # Calculate sum of parts and then water_loss, ensuring non-negative water_loss\n df['sum_parts_weight'] = df['Shucked_weight'] + df['Viscera_weight'] + df['Shell_weight']\n df['water_loss'] = df['Whole_weight'] - df['sum_parts_weight']\n df['water_loss'] = np.clip(df['water_loss'], 0, None) # Clip to ensure non-negative water loss\n df.drop(columns=['sum_parts_weight'], errors='ignore', inplace=True)\n\n\n # Align columns between training and test sets after feature engineering\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n # Add missing columns to test_df and fill with NaN\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n # Check for columns in test_df not in train_df (shouldn't happen with consistent FE)\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n # Ensure columns are in the same order\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Define and return a ColumnTransformer for data preprocessing.\n \"\"\"\n x_train = data.x_train\n\n # Dynamically identify feature types\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Define and return an instantiated, Scikit-Learn compatible model.\n \"\"\"\n # XGBRegressor with 'reg:squaredlogerror' objective as per plan.\n # Other parameters will be tuned via GridSearchCV.\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', # Metric to monitor during training/evaluation\n random_state=config.RANDOM_STATE,\n n_jobs=-1, # Use all available cores\n tree_method='hist', # Default method, will be overridden by param_grid for tuning\n # Set verbosity to 0 to suppress output from individual fits during grid search\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV, focusing on tree_method optimization.\n \"\"\"\n # The grid focuses on 'tree_method' ('exact' or 'hist') and 'max_bin' for 'hist'.\n # XGBoost ignores 'max_bin' if 'tree_method' is 'exact'.\n # Total combinations: 2 * 2 * 2 * 2 * 2 * 2 * 2 = 128\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], # Relevant when 'tree_method' is 'hist'\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric. For RMSLE, use 'neg_mean_squared_log_error' for GridSearchCV.\n \"\"\"\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n \"\"\"\n Define and return an instantiated cross-validation splitter.\n KFold is chosen as a robust general-purpose splitter for regression,\n also aligning with some top solutions' findings for this specific problem.\n \"\"\"\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n Predictions are clipped to a valid range, and not rounded to integers.\n \"\"\"\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n # Post-processing: Clip predictions to be within the known range of 'Rings'.\n # As per competition analysis, do NOT round predictions to integers,\n # as RMSLE is often optimized by floating-point predictions.\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14822415063087008} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb # Specific import for XGBoost\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# Type aliases and protocols as provided in the problem description\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Define ALL constants and configuration flags for the script.\n \"\"\"\n config = types.SimpleNamespace()\n\n # File and Directory Settings\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\" # For format reference\n\n # Column Definitions\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = [] # No additional columns to drop beyond ID and TARGET\n\n # General Settings\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5 # Number of folds for cross-validation\n\n # Feature Engineering Specifics\n # Numerical features to interact with 'Sex'\n config.INTERACTION_FEATURES = [\n 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight'\n ]\n\n # Target-specific post-processing\n config.TARGET_MIN = 1.0 # Minimum Rings value observed in dataset\n config.TARGET_MAX = 29.0 # Maximum Rings value observed in dataset\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Load data, separate target, perform feature engineering, and align columns.\n \"\"\"\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n # Validate essential columns exist\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n # Preserve test IDs\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Separate target variable\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n # Drop ID and target columns from training data\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n # Drop ID column from test data\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # --- Feature Engineering ---\n # Handle physically impossible 0 values by replacing with NaN for imputation\n columns_to_check_for_zeros = [\n 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight'\n ]\n for df in [data.x_train, data.x_test]:\n for col in columns_to_check_for_zeros:\n if col in df.columns:\n df[col] = df[col].replace(0, np.nan)\n\n # One-hot encode 'Sex'\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n # Ensure all possible 'Sex' categories exist in both train and test after get_dummies\n # This prevents column mismatch if a category is absent in one set.\n # Based on the Abalone dataset, categories are 'M', 'F', 'I'.\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0 # Add missing column with all zeros\n\n # Create Sex-specific interaction features\n for sex_cat in sex_dummies_expected:\n if sex_cat in data.x_train.columns: # Ensure the OHE column exists\n for feature in config.INTERACTION_FEATURES:\n if feature in data.x_train.columns: # Ensure the base numerical feature exists\n new_feature_name = f\"{sex_cat}_{feature}\"\n data.x_train[new_feature_name] = data.x_train[sex_cat] * data.x_train[feature]\n data.x_test[new_feature_name] = data.x_test[sex_cat] * data.x_test[feature]\n\n # Align columns between x_train and x_test after all feature engineering\n # This ensures both DataFrames have the same columns in the same order.\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n # Add columns missing in test_df to test_df, filling with 0\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n # Add columns missing in train_df to train_df, filling with 0 (less common but robust)\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n # Reorder test_df columns to match train_df columns\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Define and return a ColumnTransformer for data preprocessing.\n \"\"\"\n # After load_data, all features (original numerical, one-hot encoded 'Sex',\n # and interaction features) are numerical.\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), # Robust to outliers\n ('scaler', sklearn.preprocessing.StandardScaler()) # Standard scaling\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough' # Any columns not specified will be passed through\n # (should be empty given robust column alignment)\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Define and return an instantiated, Scikit-Learn compatible XGBoost model.\n \"\"\"\n # XGBoost with native objective='reg:squaredlogerror'\n # This objective minimizes MSLE directly, and its predict method\n # will output values in the original scale (after expm1 transformation).\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1, # Use all available cores for parallel processing\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV.\n The total number of parameter combinations should be between 10-100.\n \"\"\"\n # This grid results in 3 * 2 * 2 * 2 * 2 * 2 * 2 = 96 combinations,\n # which is within the recommended range.\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300], # Number of boosting rounds\n 'model__learning_rate': [0.05, 0.1], # Step size shrinkage\n 'model__max_depth': [5, 7], # Maximum depth of a tree\n 'model__subsample': [0.7, 0.9], # Subsample ratio of the training instance\n 'model__colsample_bytree': [0.7, 0.9], # Subsample ratio of columns when constructing each tree\n 'model__reg_alpha': [0.1, 1.0], # L1 regularization term on weights\n 'model__reg_lambda': [0.1, 1.0], # L2 regularization term on weights\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n \"\"\"\n Custom RMSLE scorer function for sklearn.metrics.make_scorer.\n Clips predictions to ensure non-negativity and minimum of 1.0 as per problem domain.\n \"\"\"\n # Ensure predictions are non-negative and at least config.TARGET_MIN (1.0 for 'Rings').\n # XGBoost's 'reg:squaredlogerror' objective implicitly handles non-negativity,\n # but explicit clipping to 1.0 ensures predictions align with the target's lower bound.\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric. For this competition, it's Root Mean Squared Logarithmic Error (RMSLE).\n \"\"\"\n # Create a custom scorer using make_scorer, indicating that a lower score is better.\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n \"\"\"\n Define and return an instantiated cross-validation splitter.\n \"\"\"\n # For regression tasks, KFold is generally appropriate.\n # Shuffling and setting a random_state ensure reproducibility.\n # The forum digest indicated that StratifiedKFold might not work well for this specific\n # regression problem, so KFold is a safe and common choice.\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n \"\"\"\n # Make predictions on the preprocessed test data using the best model from GridSearchCV.\n predictions = model.predict(data.x_test)\n\n # Post-processing: Clip predictions to the valid range [TARGET_MIN, TARGET_MAX].\n # The 'Rings' target is an integer between 1 and 29.\n # As per the forum digest, do NOT round predictions to integers, keep them as floats.\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n # Create submission DataFrame with 'id' and 'Rings' columns.\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.1478008841544463} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Type error.\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Type error.\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Attribute error.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.14778357751251783} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Type error.\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Type error.\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Attribute error.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n print(f\"Process terminated: {e}\")", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Type error.\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Type error.\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Attribute error.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.1492298511262347} +{"x": "s4e4\n# General imports\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, # Corrected typo here\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\n# Type alias for any valid scikit-learn CV splitter instance\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, # Corrected typo here\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str # one of the named scorers in sklearn.metrics.\n\n@runtime_checkable\nclass Scorer(Protocol):\n \"\"\"\n A protocol for any callable object that returns a float score.\n\n This is general and runtime checkable, covering both functions\n and callable class instances.\n \"\"\"\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n# Specific imports for MLPRegressor\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.model_selection import ParameterGrid\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Defines all constants and configuration flags for the script.\n \"\"\"\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5 # For KFold cross-validation\n # Per instructions, no complex manual or automated feature engineering\n # This explicitly excludes any tree-based models, np.log1p target transformation, and sophisticated ensembling.\n # Therefore, no COLS_TO_DROP beyond ID/TARGET.\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Loads and preprocesses the training and test data.\n Separates the target variable and preserves test IDs.\n Applies minimal, specified drops.\n \"\"\"\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n if not hasattr(config, 'INPUT_DIR') or not isinstance(config.INPUT_DIR, pathlib.Path):\n raise AttributeError(\"config.INPUT_DIR must be a pathlib.Path\")\n if not hasattr(config, 'TRAIN_FILE') or not isinstance(config.TRAIN_FILE, str):\n raise AttributeError(\"config.TRAIN_FILE must be a string\")\n if not hasattr(config, 'TEST_FILE') or not isinstance(config.TEST_FILE, str):\n raise AttributeError(\"config.TEST_FILE must be a string\")\n if not hasattr(config, 'ID_COLUMN') or not isinstance(config.ID_COLUMN, str):\n raise AttributeError(\"config.ID_COLUMN must be a string\")\n if not hasattr(config, 'TARGET_COLUMN') or not isinstance(config.TARGET_COLUMN, str):\n raise AttributeError(\"config.TARGET_COLUMN must be a string\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n try:\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure data files are located at {config.INPUT_DIR}. Error: {e}\")\n except Exception as e:\n raise RuntimeError(f\"Failed to load dataframes from CSV files: {e}\")\n\n data = types.SimpleNamespace()\n\n # Handle NaNs in target variable by dropping rows, as model fitting will fail.\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target column.\")\n if train_df.empty:\n raise ValueError(\"Training data became empty after dropping rows with NaN target values.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n # Robustly drop ID and TARGET columns from training features\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns], errors='ignore')\n\n # Preserve test IDs BEFORE dropping the ID column from test features\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID_COLUMN '{config.ID_COLUMN}' not found in the test DataFrame.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Robustly drop ID column from test features\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns], errors='ignore')\n\n # Ensure x_train and x_test have the exact same columns in the same order.\n # This is crucial for ColumnTransformer and model prediction consistency.\n if not data.x_train.columns.equals(data.x_test.columns):\n # Identify columns present in train but not test, and vice versa\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n missing_in_train = list(test_cols - train_cols)\n\n # Add missing columns to x_test (fill with 0 or NaN, SimpleImputer will handle it)\n for col in missing_in_test:\n if data.x_train[col].dtype in ['object', 'category']:\n # For categorical features, adding a 'missing' category or using a placeholder\n # This simple setup does not use complex FE, so using 0 or nan is a placeholder.\n # OneHotEncoder(handle_unknown='ignore') usually handles unseen categories.\n data.x_test[col] = 0 # Or np.nan, if a numerical imputer is used on it.\n else:\n data.x_test[col] = 0 # Or np.nan\n print(f\"Warning: Column '{col}' found in x_train but not x_test. Added to x_test with default value.\")\n\n # Add missing columns to x_train (less common, but for robustness)\n for col in missing_in_train:\n if data.x_test[col].dtype in ['object', 'category']:\n data.x_train[col] = 0\n else:\n data.x_train[col] = 0\n print(f\"Warning: Column '{col}' found in x_test but not x_train. Added to x_train with default value.\")\n\n # Ensure column order matches x_train\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Defines and returns a ColumnTransformer for data preprocessing.\n Uses StandardScaler for numerical features and OneHotEncoder for categorical features.\n \"\"\"\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(\"data must be a types.SimpleNamespace\")\n if not hasattr(data, 'x_train') or not isinstance(data.x_train, pd.DataFrame):\n raise AttributeError(\"data.x_train must be a pandas.DataFrame containing features.\")\n if data.x_train.empty:\n raise ValueError(\"data.x_train is empty, cannot determine features for preprocessing.\")\n\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not numerical_features and not categorical_features:\n raise ValueError(\"No numerical or categorical features found in x_train. Preprocessor cannot be defined.\")\n\n # Pipeline for numerical features: Impute missing values with median, then scale\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n # Pipeline for categorical features: Impute missing values with most frequent, then one-hot encode\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n # Combine transformers using ColumnTransformer\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' # Keep any other columns if they exist and are not handled\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Define and return an instantiated, Scikit-Learn compatible Feed-Forward Neural Network (MLP) model.\n \"\"\"\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n if not hasattr(config, 'RANDOM_STATE') or not isinstance(config.RANDOM_STATE, int):\n raise AttributeError(\"config.RANDOM_STATE must be an integer for reproducibility.\")\n\n # Using MLPRegressor for direct Rings regression as per instructions.\n # No np.log1p target transformation is allowed.\n return MLPRegressor(\n random_state=config.RANDOM_STATE,\n solver='adam', # 'adam' is generally a robust and efficient default optimizer\n max_iter=1000, # Increased iterations for better convergence with potential early stopping\n early_stopping=True, # Stop training if validation score is not improving\n n_iter_no_change=20, # Number of iterations with no improvement to wait before stopping\n tol=1e-4, # Tolerance for the optimization\n verbose=False # Set to True for debugging training progress\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV for MLPRegressor.\n The grid aims for an intelligent numerical range with 2-3 choices per parameter,\n keeping the total combinations within the 10-100 range.\n \"\"\"\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n # Define a reasonable hyperparameter grid for MLPRegressor.\n # Total combinations: 3 * 2 * 2 = 12, which is well within the 10-100 limit.\n return ParameterGrid({\n 'model__hidden_layer_sizes': [(64, 32), (100, 50), (128, 64)], # Explore 2-layer architectures\n 'model__activation': ['relu', 'tanh'], # Common non-linear activation functions\n 'model__alpha': [0.0001, 0.001], # L2 penalty (regularization term)\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n \"\"\"\n Provide the scoring metric. RMSLE is the competition metric.\n GridSearchCV maximizes by default, so 'neg_mean_squared_log_error' is used.\n \"\"\"\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n # The competition metric is Root Mean Squared Logarithmic Error (RMSLE).\n # GridSearchCV optimizes by maximizing the score.\n # 'neg_mean_squared_log_error' minimizes the Mean Squared Logarithmic Error (MSLE),\n # which is equivalent to minimizing RMSLE (since sqrt is monotonic).\n # The instruction \"explicitly excludes any ... np.log1p target transformation\"\n # means we apply the MSLE metric directly on the raw 'Rings' predictions.\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n \"\"\"\n Define and return an instantiated cross-validation splitter.\n Using KFold for this regression task.\n \"\"\"\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n if not hasattr(config, 'N_SPLITS') or not isinstance(config.N_SPLITS, int):\n raise AttributeError(\"config.N_SPLITS must be an integer for the CV splitter.\")\n if not hasattr(config, 'RANDOM_STATE') or not isinstance(config.RANDOM_STATE, int):\n raise AttributeError(\"config.RANDOM_STATE must be an integer for reproducibility.\")\n\n # KFold is appropriate for regression tasks.\n # Shuffle ensures randomness in splits, and random_state ensures reproducibility.\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n Applies essential post-processing (clipping) to predictions.\n \"\"\"\n # Validate inputs\n if not (isinstance(model, Model) or isinstance(model, sklearn.pipeline.Pipeline)):\n raise TypeError(\"model must adhere to Model protocol or be a sklearn.pipeline.Pipeline.\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(\"data must be a types.SimpleNamespace.\")\n if not hasattr(data, 'x_test') or not isinstance(data.x_test, pd.DataFrame):\n raise AttributeError(\"data.x_test must be a pandas.DataFrame containing test features.\")\n if data.x_test.empty:\n raise ValueError(\"data.x_test is empty, cannot generate predictions.\")\n if not hasattr(data, 'test_ids') or not isinstance(data.test_ids, pd.Series):\n raise AttributeError(\"data.test_ids must be a pandas.Series containing test IDs.\")\n if not hasattr(config, 'ID_COLUMN') or not isinstance(config.ID_COLUMN, str):\n raise AttributeError(\"config.ID_COLUMN must be a string.\")\n if not hasattr(config, 'TARGET_COLUMN') or not isinstance(config.TARGET_COLUMN, str):\n raise AttributeError(\"config.TARGET_COLUMN must be a string.\")\n\n # Generate raw predictions from the best trained model\n predictions = model.predict(data.x_test)\n\n # Validate prediction output\n if not isinstance(predictions, np.ndarray):\n raise TypeError(f\"Model predictions are of type {type(predictions)}, expected numpy.ndarray.\")\n if predictions.ndim != 1:\n # If the model returns a 2D array with a single column (e.g., [[val1], [val2]]), flatten it\n if predictions.ndim == 2 and predictions.shape[1] == 1:\n predictions = predictions.flatten()\n else:\n raise ValueError(f\"Predictions array has unexpected shape: {predictions.shape}. Expected 1D array.\")\n\n # Post-processing: Clip predictions to the known valid range of 'Rings' (1 to 29).\n # This prevents physically impossible predictions and is a standard safe practice.\n # The competition's RMSLE metric and forum insights suggest keeping predictions as floats.\n predictions = np.clip(predictions, 1, 29)\n\n # Create the submission DataFrame\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1506252966792615} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import StratifiedKFold, ParameterGrid\nimport lightgbm as lgb\nfrom sklearn.preprocessing import KBinsDiscretizer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n# Type alias for any valid scikit-learn CV splitter instance\nCVSplitter = Union[\n StratifiedKFold\n]\n\n# --- Protocols for type safety --- (Defined in prompt, included for completeness)\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str # one of the named scorers in sklearn.metrics.\n\n@runtime_checkable\nclass Scorer(Protocol):\n \"\"\"\n A protocol for any callable object that returns a float score.\n \"\"\"\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Defines all constants and configuration flags for the script.\n \"\"\"\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, # Number of splits for cross-validation\n N_BINS=15, # Number of bins for target discretization\n MIN_RINGS_PREDICTION=1, # Minimum possible Rings value for clipping\n MAX_RINGS_PREDICTION=29, # Maximum possible Rings value for clipping\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Loads training and test data, performs basic cleaning and target discretization.\n\n Args:\n config (types.SimpleNamespace): Configuration object containing file paths and column names.\n\n Returns:\n types.SimpleNamespace: A namespace containing x_train, y_train (binned),\n y_train_original, x_test, test_ids, and bin_to_mean_rings mapping.\n Raises:\n ValueError: If essential files are not found.\n \"\"\"\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n # Drop rows with NaN in the target column from train_df\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n # Store original target for bin mean calculation\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n\n # Handle 'Height' == 0 by replacing with NaN, as it's physically impossible.\n # This will be handled by the imputer in the preprocessor.\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n # Discretize the target variable ('Rings') into bins for classification\n unique_rings = data.y_train_original.nunique()\n n_bins_actual = min(config.N_BINS, unique_rings)\n if n_bins_actual < config.N_BINS:\n print(f\"Warning: Adjusting N_BINS from {config.N_BINS} to {n_bins_actual} due to fewer unique target values ({unique_rings}).\")\n\n if n_bins_actual < 2:\n raise ValueError(\"Cannot perform target discretization with fewer than 2 bins. Check target distribution or N_BINS.\")\n\n discretizer = KBinsDiscretizer(\n n_bins=n_bins_actual,\n encode='ordinal',\n strategy='quantile', # 'quantile' strategy aims for equal-sized bins\n subsample=None, # Use all data to determine bins\n random_state=config.RANDOM_STATE\n )\n\n # KBinsDiscretizer expects a 2D array, so convert series to DataFrame\n y_train_binned = discretizer.fit_transform(data.y_train_original.to_frame())\n data.y_train = y_train_binned.ravel().astype(int) # Flatten and convert to int for classifier\n\n # Calculate mean Rings for each bin, to be used in get_submission\n data.bin_to_mean_rings = {}\n temp_df = pd.DataFrame({'original_rings': data.y_train_original, 'bin': data.y_train})\n for bin_idx in range(n_bins_actual):\n bin_values = temp_df.loc[temp_df['bin'] == bin_idx, 'original_rings']\n if not bin_values.empty:\n data.bin_to_mean_rings[bin_idx] = bin_values.mean()\n else:\n # Fallback for empty bins: use the overall mean. This should be rare with 'quantile' strategy.\n print(f\"Warning: Bin {bin_idx} is empty after discretization. Using overall mean for its representative value.\")\n data.bin_to_mean_rings[bin_idx] = data.y_train_original.mean()\n\n # Robustly drop ID and target columns from training data\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n # Preserve test IDs and robustly drop ID column from test data\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n # Align columns: Ensure x_test has the exact same columns as x_train in the same order.\n # Add missing columns to x_test, filling with NaN (preprocessor will handle these).\n # Drop any columns from x_test that are not present in x_train.\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final] # Ensure order is the same and drop extra columns\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Defines and returns a ColumnTransformer for data preprocessing.\n\n Args:\n config (types.SimpleNamespace): Configuration object.\n data (types.SimpleNamespace): Data object containing x_train for feature type identification.\n\n Returns:\n Processor: A sklearn.compose.ColumnTransformer instance.\n \"\"\"\n x_train = data.x_train\n\n # Dynamically identify numerical and categorical features\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n # Preprocessing pipeline for numerical features: impute NaNs, then scale\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n # Preprocessing pipeline for categorical features: impute NaNs, then one-hot encode\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n # Create a column transformer to apply different transformations to different columns\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' # Keep any other columns as they are (e.g., if there were boolean or custom types)\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> ClassifierModel:\n \"\"\"\n Defines and returns an instantiated, Scikit-Learn compatible classifier model.\n\n Args:\n config (types.SimpleNamespace): Configuration object.\n\n Returns:\n ClassifierModel: An LGBMClassifier instance.\n \"\"\"\n # Use LGBMClassifier to predict the target bins\n # `num_class` is inferred by LGBM from the target `y`\n return lgb.LGBMClassifier(\n objective='multiclass',\n random_state=config.RANDOM_STATE,\n n_jobs=-1, # Use all available cores\n verbose=-1 # Suppress verbose output\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n \"\"\"\n Specifies a hyperparameter grid for GridSearchCV.\n\n Args:\n config (types.SimpleNamespace): Configuration object.\n\n Returns:\n ParameterGrid: A ParameterGrid instance defining the hyperparameter search space.\n \"\"\"\n # Define a hyperparameter grid for the LGBMClassifier.\n # The total number of combinations is 3*2*2*2*2*2 = 96, which is reasonable.\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300], # Number of boosting rounds\n 'model__learning_rate': [0.05, 0.1], # Step size shrinkage\n 'model__num_leaves': [20, 31], # Maximum number of leaves in one tree\n 'model__max_depth': [5, 7], # Maximum tree depth\n 'model__reg_alpha': [0.1, 0.5], # L1 regularization term on weights\n 'model__reg_lambda': [0.1, 0.5], # L2 regularization term on weights\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n \"\"\"\n Provides the scoring metric for GridSearchCV.\n\n Args:\n config (types.SimpleNamespace): Configuration object.\n\n Returns:\n ScorerString: A string identifier for a scikit-learn scoring metric.\n \"\"\"\n # For a classifier predicting bins, accuracy is a direct and suitable metric for GridSearchCV\n # The final regression-style evaluation (RMSLE) is handled in get_submission.\n return 'accuracy'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n \"\"\"\n Defines and returns an instantiated cross-validation splitter.\n\n Args:\n config (types.SimpleNamespace): Configuration object.\n\n Returns:\n CVSplitter: A StratifiedKFold instance.\n \"\"\"\n # Use StratifiedKFold because the target is now binned categories, ensuring\n # that each fold has a similar distribution of these target bins.\n return StratifiedKFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Generates the final submission DataFrame.\n\n Args:\n model (sklearn.pipeline.Pipeline): The trained pipeline (best estimator from GridSearchCV).\n data (types.SimpleNamespace): Data object containing x_test, test_ids, and bin_to_mean_rings mapping.\n config (types.SimpleNamespace): Configuration object.\n\n Returns:\n pd.DataFrame: A DataFrame with 'id' and 'Rings' columns ready for submission.\n \"\"\"\n # Predict probabilities for each bin on the test set\n # The model is a classifier, so use predict_proba to get probabilities for each class (bin)\n bin_probabilities = model.predict_proba(data.x_test)\n\n # Get the most probable bin index for each test sample\n predicted_bin_indices = np.argmax(bin_probabilities, axis=1)\n\n # Map the predicted bin index back to the mean Rings value for that bin\n # Use a vectorized lookup for efficiency and robustness\n predictions = np.array([data.bin_to_mean_rings[bin_idx] for bin_idx in predicted_bin_indices])\n\n # Post-processing: Clip predictions to the valid range [1, 29]\n # This prevents physically impossible predictions and is a common practice.\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n # The evaluation metric (RMSLE) does not require integer predictions,\n # and rounding to integer often harms the score, so predictions remain float.\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1741984448954745} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1638192504568697} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric.\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline([\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except FileNotFoundError:\n pass", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1638192504568697} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']: \n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1494516975914041} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14932029146725184} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [25, 31],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14927348955176137} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14918930478676834} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[],\n INTERACTION_FEATURES=['Shell_weight', 'Diameter', 'Length', 'Whole_weight', 'Shucked_weight']\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']: \n df_fe[f'Sex_{s_cat}'] = 0\n\n for col_name in config.INTERACTION_FEATURES:\n if col_name in df_fe.columns: \n if f'Sex_I' in df_fe.columns:\n df_fe[f'Sex_I_x_{col_name}'] = df_fe['Sex_I'] * df_fe[col_name]\n if f'Sex_M' in df_fe.columns:\n df_fe[f'Sex_M_x_{col_name}'] = df_fe['Sex_M'] * df_fe[col_name]\n if f'Sex_F' in df_fe.columns: \n df_fe[f'Sex_F_x_{col_name}'] = df_fe['Sex_F'] * df_fe[col_name]\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14930895139295186} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n if 'Height' in data.x_train.columns:\n data.x_train.loc[data.x_train['Height'] == 0, 'Height'] = np.nan\n if 'Height' in data.x_test.columns:\n data.x_test.loc[data.x_test['Height'] == 0, 'Height'] = np.nan\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14906103261619333} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200], \n 'model__learning_rate': [0.05, 0.1], \n 'model__max_depth': [5, 7], \n 'model__min_samples_leaf': [20, 40], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n raise FileNotFoundError(f\"Data files not found in {config.INPUT_DIR}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column {config.TARGET_COLUMN} not found in training data.\")\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n if train_df.empty:\n raise ValueError(\"Training data is empty after removing NaNs from target.\")\n\n # Feature Engineering\n for df in [train_df, test_df]:\n if 'Height' in df.columns and 'Length' in df.columns and 'Diameter' in df.columns:\n df['Volume'] = df['Height'] * df['Length'] * df['Diameter']\n if 'Shucked weight' in df.columns and 'Whole weight' in df.columns:\n df['Shucked_Ratio'] = df['Shucked weight'] / (df['Whole weight'] + 1e-9)\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=0)\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Train and Test columns do not match after alignment.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not numerical_features and not categorical_features:\n raise ValueError(\"No features found for preprocessing.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n transformers = []\n if numerical_features:\n transformers.append(('num', numerical_transformer, numerical_features))\n if categorical_features:\n transformers.append(('cat', categorical_transformer, categorical_features))\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=transformers,\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [10, 20],\n 'model__l2_regularization': [0.0, 1.0],\n 'model__max_iter': [100, 200]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test') or data.x_test.empty:\n raise ValueError(\"Test data is missing or empty.\")\n\n predictions = model.predict(data.x_test)\n\n if isinstance(predictions, np.ndarray) and predictions.ndim > 1:\n predictions = predictions.flatten()\n\n # Rings are typically positive counts; clipping to valid abalone range [1, 29]\n predictions = np.clip(predictions, 1.0, 29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14941854825156697} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n\n HEIGHT_ZERO_REPLACE_NAN=True, \n\n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n\n TRANSFORM_TARGET_LOG1P=True, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200], \n 'model__learning_rate': [0.05, 0.1], \n 'model__max_depth': [5, 7], \n 'model__min_samples_leaf': [20, 40], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test).flatten()\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482733183174271} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n\n HEIGHT_ZERO_REPLACE_NAN=True, \n\n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n\n TRANSFORM_TARGET_LOG1P=True, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15001320435727195} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15001320435727195} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef engineer_features(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n if all(c in df.columns for c in ['Length', 'Diameter', 'Height']):\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Surface_Area'] = 2 * (df['Length'] * df['Diameter'] + df['Length'] * df['Height'] + df['Diameter'] * df['Height'])\n\n weight_cols = ['Shucked weight', 'Viscera weight', 'Shell weight']\n if all(c in df.columns for c in weight_cols):\n df['Weight_Sum'] = df[weight_cols].sum(axis=1)\n if 'Whole weight' in df.columns:\n df['Weight_Diff'] = df['Whole weight'] - df['Weight_Sum']\n df['Shell_Ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n df['Meat_Yield'] = df['Shucked weight'] / (df['Whole weight'] + 1e-9)\n\n return df\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n train_df = engineer_features(train_df)\n test_df = engineer_features(test_df)\n\n data = types.SimpleNamespace()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]).values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n preds_log = model.predict(data.x_test)\n if hasattr(preds_log, 'reshape'):\n preds_log = preds_log.reshape(-1)\n\n predictions = np.expm1(preds_log)\n predictions = np.clip(predictions, 0.5, 29.5)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14720303804632368} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14932029146725184} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']: \n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14936113907184126} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200], \n 'model__learning_rate': [0.05, 0.1], \n 'model__max_depth': [5, 7], \n 'model__min_samples_leaf': [20, 40], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489900466550185} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489900466550185} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [25, 31],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14917739014253276} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan)\n else:\n df_fe['Height'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [25, 31],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14925318891874398} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\nclass StackingRegressor(BaseEstimator, RegressorMixin):\n def __init__(self, base_models: List[Model], meta_model: Model, n_splits: int = 5, random_state: int = 42):\n if not isinstance(base_models, list) or not all(isinstance(m, BaseEstimator) for m in base_models):\n raise TypeError(\"base_models must be a list of scikit-learn estimators.\")\n if not isinstance(meta_model, BaseEstimator):\n raise TypeError(\"meta_model must be a scikit-learn estimator.\")\n if not isinstance(n_splits, int) or n_splits < 2:\n raise ValueError(\"n_splits must be an integer >= 2.\")\n if not isinstance(random_state, int):\n raise ValueError(\"random_state must be an integer.\")\n\n self.base_models = base_models\n self.meta_model = meta_model\n self.n_splits = n_splits\n self.random_state = random_state\n\n self.fitted_base_models_full_data_ = None\n self.meta_model_ = None\n\n def fit(self, X: pd.DataFrame, y: pd.Series):\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if not isinstance(y, (pd.Series, np.ndarray)):\n raise TypeError(\"y must be a pandas Series or numpy array.\")\n if X.shape[0] != y.shape[0]:\n raise ValueError(\"X and y must have the same number of samples.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n y_arr = y.to_numpy() if isinstance(y, pd.Series) else y\n\n n_samples = X_arr.shape[0]\n n_base_models = len(self.base_models)\n\n oof_predictions = np.zeros((n_samples, n_base_models))\n\n kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state)\n\n for i, (train_idx, val_idx) in enumerate(kf.split(X_arr, y_arr)):\n X_train_fold, y_train_fold = X_arr[train_idx], y_arr[train_idx]\n X_val_fold = X_arr[val_idx]\n\n for j, base_model_orig in enumerate(self.base_models):\n model = clone(base_model_orig)\n model.fit(X_train_fold, y_train_fold)\n oof_predictions[val_idx, j] = model.predict(X_val_fold)\n\n self.meta_model_ = clone(self.meta_model)\n self.meta_model_.fit(oof_predictions, y_arr)\n\n self.fitted_base_models_full_data_ = []\n for base_model_orig in self.base_models:\n model = clone(base_model_orig)\n model.fit(X_arr, y_arr)\n self.fitted_base_models_full_data_.append(model)\n\n return self\n\n def predict(self, X: pd.DataFrame) -> np.ndarray:\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if self.fitted_base_models_full_data_ is None or self.meta_model_ is None:\n raise RuntimeError(\"StackingRegressor not fitted. Call fit() first.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n\n n_test_samples = X_arr.shape[0]\n n_base_models = len(self.fitted_base_models_full_data_)\n\n base_test_predictions = np.zeros((n_test_samples, n_base_models))\n for j, model in enumerate(self.fitted_base_models_full_data_):\n base_test_predictions[:, j] = model.predict(X_arr)\n\n final_predictions = self.meta_model_.predict(base_test_predictions)\n final_predictions = np.maximum(0, final_predictions)\n\n return final_predictions\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n xgbr = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n base_models = [lgbm, xgbr, hgbm]\n meta_model = Ridge(random_state=config.RANDOM_STATE)\n\n stacking_regressor = StackingRegressor(\n base_models=base_models,\n meta_model=meta_model,\n n_splits=config.N_STACKING_FOLDS,\n random_state=config.RANDOM_STATE\n )\n return stacking_regressor\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__meta_model__alpha': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.149774659090181} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef engineer_features(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Creates domain-specific features for Abalone dataset.\"\"\"\n df = df.copy()\n\n # Height = 0 is a known data error in Abalone\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n # Basic Geometric Features\n if all(c in df.columns for c in ['Length', 'Diameter', 'Height']):\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Surface_Area'] = 2 * (df['Length'] * df['Diameter'] + df['Length'] * df['Height'] + df['Diameter'] * df['Height'])\n\n # Weight components logic\n weight_cols = ['Shucked weight', 'Viscera weight', 'Shell weight']\n if all(c in df.columns for c in weight_cols):\n df['Weight_Sum'] = df[weight_cols].sum(axis=1)\n if 'Whole weight' in df.columns:\n df['Weight_Diff'] = df['Whole weight'] - df['Weight_Sum']\n df['Shell_Ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n df['Meat_Yield'] = df['Shucked weight'] / (df['Whole weight'] + 1e-9)\n\n return df\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Standardize Playground column names immediately to prevent duplicates during augmentation\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n\n # Data augmentation with original Abalone dataset\n original_paths = [\n pathlib.Path(\"/kaggle/input/abalone-dataset/abalone.csv\"),\n config.INPUT_DIR / \"abalone.csv\"\n ]\n for opath in original_paths:\n if opath.exists():\n original_df = pd.read_csv(opath)\n # Standardize names to match PS dataset\n original_df.columns = [c.replace('_', ' ').capitalize() if c != 'Whole_weight' else 'Whole weight' for c in original_df.columns]\n # Manual corrections for capitalization or specific mismatches\n original_df = original_df.rename(columns={\n 'Rings': config.TARGET_COLUMN, \n 'Shucked weight': 'Shucked weight', \n 'Viscera weight': 'Viscera weight',\n 'whole weight': 'Whole weight'\n })\n # Combine and reset index to avoid any duplicate label issues\n train_df = pd.concat([train_df, original_df], ignore_index=True)\n break\n\n # Critical: Remove duplicate column names if any were introduced\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n\n # Drop rows where target is missing\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n # Apply feature engineering\n train_df = engineer_features(train_df)\n test_df = engineer_features(test_df)\n\n data = types.SimpleNamespace()\n # Log transform target for RMSLE competition optimization\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]).values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Drop ID and Target columns robustly\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n # Align columns\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n preds_log = model.predict(data.x_test)\n if hasattr(preds_log, 'reshape'):\n preds_log = preds_log.reshape(-1)\n\n predictions = np.expm1(preds_log)\n predictions = np.clip(predictions, 0.5, 29.5)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14730544647396226} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n raise FileNotFoundError(f\"Data files not found in {config.INPUT_DIR}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column {config.TARGET_COLUMN} not found in training data.\")\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n if train_df.empty:\n raise ValueError(\"Training data is empty after removing NaNs from target.\")\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=0)\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Train and Test columns do not match after alignment.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not numerical_features and not categorical_features:\n raise ValueError(\"No features found for preprocessing.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n transformers = []\n if numerical_features:\n transformers.append(('num', numerical_transformer, numerical_features))\n if categorical_features:\n transformers.append(('cat', categorical_transformer, categorical_features))\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=transformers,\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [10, 20],\n 'model__l2_regularization': [0.0, 1.0],\n 'model__max_iter': [100, 200]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test') or data.x_test.empty:\n raise ValueError(\"Test data is missing or empty.\")\n\n predictions = model.predict(data.x_test)\n\n if isinstance(predictions, np.ndarray) and predictions.ndim > 1:\n predictions = predictions.flatten()\n\n predictions = np.clip(predictions, 1.0, 29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14913210699585525} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformations.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634024615937277} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found: {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found: {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Handle NaNs in Target\n initial_len = len(train_df)\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n if len(train_df) < initial_len:\n print(f\"Dropped {initial_len - len(train_df)} rows with NaN target.\")\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Preservation of IDs\n if config.ID_COLUMN not in test_df.columns:\n raise KeyError(f\"ID column {config.ID_COLUMN} missing from test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Feature Engineering: Ratio of weights\n for df in [train_df, test_df]:\n df['Shell_Weight_Ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n df['Viscera_Weight_Ratio'] = df['Whole weight.1'] / (df['Whole weight'] + 1e-9)\n df['Shucked_Weight_Ratio'] = df['Whole weight.2'] / (df['Whole weight'] + 1e-9)\n df['Surface_Area'] = df['Length'] * df['Diameter']\n\n # Define columns to drop\n drop_cols_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n \n cols_to_drop_train = [c for c in drop_cols_base if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Alignment\n data.x_test = data.x_test[data.x_train.columns]\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n num_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n cat_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', num_transformer, numerical_features),\n ('cat', cat_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n # Use HistGradientBoostingRegressor for high performance and native NaN handling\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n early_stopping=True,\n max_iter=500,\n loss='squared_error'\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, 15],\n 'model__l2_regularization': [0.0, 0.1]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Abalone S4E4 uses RMSLE. neg_mean_squared_log_error is the appropriate sklearn proxy.\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(\n n_splits=config.N_SPLITS, \n shuffle=True, \n random_state=config.RANDOM_STATE\n )\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object missing x_test\")\n \n predictions = model.predict(data.x_test)\n \n # Post-processing: Rings must be positive.\n # Dataset rings typically range 1 to 29.\n predictions = np.clip(predictions, 1, 30)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14967725797986361} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n \n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n try:\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Data files not found at {config.INPUT_DIR}: {e}\")\n\n # Handle NaNs in target\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Preservation of IDs\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} missing from test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Define columns to drop\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Ensure column alignment\n if not data.x_train.columns.equals(data.x_test.columns):\n missing_in_test = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_in_test:\n data.x_test[col] = 0\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n from sklearn.ensemble import HistGradientBoostingRegressor\n return HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=300,\n early_stopping=True,\n scoring='loss',\n validation_fraction=0.1,\n n_iter_no_change=15\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 0.1]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n \n # Rings are typically 1-29 in this dataset. We must ensure no negative values for RMSLE.\n predictions = np.clip(predictions, 1, 29)\n \n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14918689106920008} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline([\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except FileNotFoundError:\n pass", "y": 0.16342083005532684} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n NUDGE_THRESHOLD_LOW=27.5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Defines and returns an instantiated, Scikit-Learn compatible LGBMRegressor model.\n \"\"\"\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n \"\"\"\n Specifies a hyperparameter grid for GridSearchCV for the LGBMRegressor.\n The grid is kept compact to stay within recommended limits.\n \"\"\"\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n predictions = np.where(\n (predictions >= config.NUDGE_THRESHOLD_LOW) & (predictions < config.TARGET_MAX),\n config.TARGET_MAX,\n predictions\n )\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n MIN_RINGS_PREDICTION=1, \n MAX_RINGS_PREDICTION=29, \n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n verbose=-1 \n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300], \n 'model__learning_rate': [0.05, 0.1], \n 'model__num_leaves': [20, 31], \n 'model__max_depth': [5, 7], \n 'model__reg_alpha': [0.1, 0.5], \n 'model__reg_lambda': [0.1, 0.5], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.feature_selection import RFE\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n N_FEATURES_TO_SELECT=20, \n\n HEIGHT_ZERO_REPLACE_NAN=True, \n\n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n\n TRANSFORM_TARGET_LOG1P=True, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\n model = RFE(\n estimator=lgbm_estimator,\n n_features_to_select=config.N_FEATURES_TO_SELECT, \n step=0.1, \n verbose=0 \n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_features_to_select': [15, 25, 35], \n 'model__step': [0.1, 0.2], \n 'model__estimator__n_estimators': [200, 400],\n 'model__estimator__learning_rate': [0.01, 0.05],\n 'model__estimator__num_leaves': [20, 31],\n 'model__estimator__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import TransformedTargetRegressor\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n model = TransformedTargetRegressor(\n regressor=lgbm_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14779170349733076} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n TRANSFORM_TARGET_LOG1P=True, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14883882951371868} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1476466239009968} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.18284564784044893} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Defines and returns an instantiated, Scikit-Learn compatible LGBMRegressor model.\n \"\"\"\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n \"\"\"\n Specifies a hyperparameter grid for GridSearchCV for the LGBMRegressor.\n The grid is kept compact to stay within recommended limits.\n \"\"\"\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n\n HEIGHT_ZERO_REPLACE_NAN=True, \n\n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.18284564784044893} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\", \n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n\n HEIGHT_ZERO_REPLACE_NAN=True, \n\n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n\n data = types.SimpleNamespace()\n\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy() \n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.18284564784044893} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric.\n We use 'neg_mean_squared_error' because GridSearchCV maximizes the score.\n \"\"\"\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN] \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n MIN_RINGS_PREDICTION=1, \n MAX_RINGS_PREDICTION=29, \n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n verbose=-1 \n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300], \n 'model__learning_rate': [0.05, 0.1], \n 'model__num_leaves': [20, 31], \n 'model__max_depth': [5, 7], \n 'model__reg_alpha': [0.1, 0.5], \n 'model__reg_lambda': [0.1, 0.5], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]).values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n preds_log = model.predict(data.x_test)\n if hasattr(preds_log, 'reshape'):\n preds_log = preds_log.reshape(-1)\n\n predictions = np.expm1(preds_log)\n predictions = np.clip(predictions, 0.5, 29.5)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14718887974976153} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n sub_weight_cols = [\"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"]\n if all(col in combined_df.columns for col in sub_weight_cols + [\"Whole_weight\"]):\n temp_sum_sub_weights = combined_df[sub_weight_cols].fillna(0).sum(axis=1)\n\n mask_whole_weight_too_small = combined_df['Whole_weight'] < temp_sum_sub_weights\n\n combined_df.loc[mask_whole_weight_too_small, 'Whole_weight'] = \\\n temp_sum_sub_weights.loc[mask_whole_weight_too_small]\n\n for sub_col in sub_weight_cols:\n mask_sub_weight_too_large = combined_df[sub_col] > combined_df['Whole_weight']\n combined_df.loc[mask_sub_weight_too_large, sub_col] = \\\n combined_df.loc[mask_sub_weight_too_large, 'Whole_weight'] \n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if config.INPUT_DIR.exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"preprocessor\", preprocessor), (\"model\", model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14944594363281627} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data = types.SimpleNamespace()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]).values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n preds_log = model.predict(data.x_test)\n if hasattr(preds_log, 'reshape'):\n preds_log = preds_log.reshape(-1)\n predictions = np.expm1(preds_log)\n predictions = np.clip(predictions, 0.5, 29.5)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14718887974976153} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n sub_weight_cols = [\"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"]\n if all(col in combined_df.columns for col in sub_weight_cols + [\"Whole_weight\"]):\n temp_sum_sub_weights = combined_df[sub_weight_cols].fillna(0).sum(axis=1)\n mask_whole_weight_too_small = combined_df['Whole_weight'] < temp_sum_sub_weights\n combined_df.loc[mask_whole_weight_too_small, 'Whole_weight'] = \\\n temp_sum_sub_weights.loc[mask_whole_weight_too_small]\n for sub_col in sub_weight_cols:\n mask_sub_weight_too_large = combined_df[sub_col] > combined_df['Whole_weight']\n combined_df.loc[mask_sub_weight_too_large, sub_col] = \\\n combined_df.loc[mask_sub_weight_too_large, 'Whole_weight']\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef engineer_features(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n return df\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n train_df = engineer_features(train_df)\n test_df = engineer_features(test_df)\n\n data = types.SimpleNamespace()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]).values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n preds_log = model.predict(data.x_test)\n if hasattr(preds_log, 'reshape'):\n preds_log = preds_log.reshape(-1)\n\n predictions = np.expm1(preds_log)\n predictions = np.clip(predictions, 0.5, 29.5)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14718887974976153} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom lightgbm import LGBMRegressor\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n try:\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure data files are located at {config.INPUT_DIR}. Error: {e}\")\n\n # Feature Engineering: Abalone-specific features\n for df in [train_df, test_df]:\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Weight_Ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n missing_cols = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_cols:\n data.x_test[col] = 0\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data.x_test is required for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n # Rings must be positive; RMSLE requires pred > -1. \n # Abalone rings range roughly from 1 to 29.\n predictions = np.clip(predictions, 1, 30)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14820353443301884} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n MIN_RINGS_PREDICTION=1, \n MAX_RINGS_PREDICTION=29, \n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n verbose=-1 \n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300], \n 'model__learning_rate': [0.05, 0.1], \n 'model__num_leaves': [20, 31], \n 'model__max_depth': [5, 7], \n 'model__reg_alpha': [0.1, 0.5], \n 'model__reg_lambda': [0.1, 0.5], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15023140282995512} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN] \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1476466239009968} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Define ALL constants and configuration flags for the script.\n \"\"\"\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Load data, separate target, and align columns.\n \"\"\"\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Define and return a ColumnTransformer for data preprocessing.\n \"\"\"\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Define and return an instantiated, Scikit-Learn compatible XGBoost model.\n \"\"\"\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV.\n \"\"\"\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n \"\"\"\n Custom RMSLE scorer function for sklearn.metrics.make_scorer.\n \"\"\"\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric.\n \"\"\"\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n \"\"\"\n Define and return an instantiated cross-validation splitter.\n \"\"\"\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n \"\"\"\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15023140282995512} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN] \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200], \n 'model__learning_rate': [0.05, 0.1], \n 'model__max_depth': [5, 7], \n 'model__min_samples_leaf': [20, 40], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200], \n 'model__learning_rate': [0.05, 0.1], \n 'model__max_depth': [5, 7], \n 'model__min_samples_leaf': [20, 40], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200], \n 'model__learning_rate': [0.05, 0.1], \n 'model__max_depth': [5, 7], \n 'model__min_samples_leaf': [20, 40], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Define ALL constants and configuration flags for the script.\n \"\"\"\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Load data, separate target, perform feature engineering, and align columns.\n \"\"\"\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Define and return a ColumnTransformer for data preprocessing.\n \"\"\"\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Define and return an instantiated, Scikit-Learn compatible XGBoost model.\n \"\"\"\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV.\n \"\"\"\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n \"\"\"\n Custom RMSLE scorer function for sklearn.metrics.make_scorer.\n \"\"\"\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric.\n \"\"\"\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n \"\"\"\n Define and return an instantiated cross-validation splitter.\n \"\"\"\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n \"\"\"\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14984411736912145} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15116091166639764} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN] \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n MIN_RINGS_VALUE=1, \n MAX_RINGS_VALUE=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category' \n else:\n data.x_test[c] = 0.0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', 'passthrough', numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200], \n 'model__learning_rate': [0.05, 0.1], \n 'model__max_depth': [5, 7], \n 'model__min_samples_leaf': [20, 40], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config_params = get_config()\n try:\n processed_data = load_data(config_params)\n data_preprocessor = get_preprocessor(config_params, processed_data)\n base_model = get_model(config_params)\n final_pipeline = sklearn.pipeline.Pipeline(steps=[\n (\"preprocessor\", data_preprocessor),\n (\"model\", base_model)\n ])\n final_pipeline.fit(processed_data.x_train, processed_data.y_train)\n submission_output = get_submission(final_pipeline, processed_data, config_params)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', StandardScaler()) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15116091166639764} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14984411736912145} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15001320435727195} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']: \n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924683333231042} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_model.fit(data.x_train, data.y_train)\n submission = get_submission(full_model, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.1503392542694313} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150, \n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1, \n reg_lambda=0.1, \n colsample_bytree=0.7, \n subsample=0.7, \n n_jobs=-1,\n verbose=-1, \n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [20, 25, 30],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14921137093492165} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [25, 31],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14917739014253276} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n return df.copy()\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return model\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [25, 31],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15001250234676175} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold] \nModel = BaseEstimator \nProcessor = sklearn.pipeline.Pipeline \nScorerString = str \n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan)\n else:\n df_fe['Height'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__num_leaves': [25, 31],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15031443851681828} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n return sklearn.preprocessing.FunctionTransformer()\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1475923941637949} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Define ALL constants and configuration flags for the script.\n \"\"\"\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Load data, separate target, perform feature engineering, and align columns.\n \"\"\"\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n columns_to_check_for_zeros = [\n 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight'\n ]\n for df in [data.x_train, data.x_test]:\n for col in columns_to_check_for_zeros:\n if col in df.columns:\n df[col] = df[col].replace(0, np.nan)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Define and return a ColumnTransformer for data preprocessing.\n \"\"\"\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Define and return an instantiated, Scikit-Learn compatible XGBoost model.\n \"\"\"\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV.\n \"\"\"\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n \"\"\"\n Custom RMSLE scorer function for sklearn.metrics.make_scorer.\n \"\"\"\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric.\n \"\"\"\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n \"\"\"\n Define and return an instantiated cross-validation splitter.\n \"\"\"\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n \"\"\"\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14773869753087515} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN] \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n try:\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Data files not found at {config.INPUT_DIR}: {e}\")\n\n # Handle NaNs in target\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Preservation of IDs\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} missing from test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Define columns to drop\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Ensure column alignment\n if not data.x_train.columns.equals(data.x_test.columns):\n missing_in_test = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_in_test:\n data.x_test[col] = 0\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n from sklearn.ensemble import HistGradientBoostingRegressor\n return HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=300,\n early_stopping=True,\n scoring='loss',\n validation_fraction=0.1,\n n_iter_no_change=15\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 0.1]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n # Rings are typically 1-29 in this dataset. We must ensure no negative values for RMSLE.\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14918689106920008} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformations.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception as e:\n pass", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformations.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n try:\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Data files not found at {config.INPUT_DIR}: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} missing from test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n if not data.x_train.columns.equals(data.x_test.columns):\n missing_in_test = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_in_test:\n data.x_test[col] = 0\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n from sklearn.ensemble import HistGradientBoostingRegressor\n return HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=300,\n early_stopping=True,\n scoring='loss',\n validation_fraction=0.1,\n n_iter_no_change=15\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 0.1]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n pipe = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n pipe.fit(dt.x_train, dt.y_train)\n sub = get_submission(pipe, dt, cfg)\n sub.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14918689106920008} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformations.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline([\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except FileNotFoundError:\n pass", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n# def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical processing.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical preprocessing.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return Ridge(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n raise FileNotFoundError(f\"Data files not found in {config.INPUT_DIR}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column {config.TARGET_COLUMN} not found in training data.\")\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n if train_df.empty:\n raise ValueError(\"Training data is empty after removing NaNs from target.\")\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=0)\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Train and Test columns do not match after alignment.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not numerical_features and not categorical_features:\n raise ValueError(\"No features found for preprocessing.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n transformers = []\n if numerical_features:\n transformers.append(('num', numerical_transformer, numerical_features))\n if categorical_features:\n transformers.append(('cat', categorical_transformer, categorical_features))\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=transformers,\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [10, 20],\n 'model__l2_regularization': [0.0, 1.0],\n 'model__max_iter': [100, 200]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test') or data.x_test.empty:\n raise ValueError(\"Test data is missing or empty.\")\n\n predictions = model.predict(data.x_test)\n\n if isinstance(predictions, np.ndarray) and predictions.ndim > 1:\n predictions = predictions.flatten()\n\n predictions = np.clip(predictions, 1.0, 29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14913210699585525} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found: {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found: {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n initial_len = len(train_df)\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n if len(train_df) < initial_len:\n print(f\"Dropped {initial_len - len(train_df)} rows with NaN target.\")\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n if config.ID_COLUMN not in test_df.columns:\n raise KeyError(f\"ID column {config.ID_COLUMN} missing from test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n drop_cols_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n \n cols_to_drop_train = [c for c in drop_cols_base if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n num_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n cat_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', num_transformer, numerical_features),\n ('cat', cat_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n early_stopping=True,\n max_iter=500,\n loss='squared_error'\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, 15],\n 'model__l2_regularization': [0.0, 0.1]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(\n n_splits=config.N_SPLITS, \n shuffle=True, \n random_state=config.RANDOM_STATE\n )\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object missing x_test\")\n \n predictions = model.predict(data.x_test)\n \n predictions = np.clip(predictions, 1, 30)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14984076250235764} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace) or not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n raise FileNotFoundError(f\"Data files not found in {config.INPUT_DIR}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column {config.TARGET_COLUMN} not found in training data.\")\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n if train_df.empty:\n raise ValueError(\"Training data is empty after removing NaNs from target.\")\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=0)\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Train and Test columns do not match after alignment.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not numerical_features and not categorical_features:\n raise ValueError(\"No features found for preprocessing.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n transformers = []\n if numerical_features:\n transformers.append(('num', numerical_transformer, numerical_features))\n if categorical_features:\n transformers.append(('cat', categorical_transformer, categorical_features))\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=transformers,\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [10, 20],\n 'model__l2_regularization': [0.0, 1.0],\n 'model__max_iter': [100, 200]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test') or data.x_test.empty:\n raise ValueError(\"Test data is missing or empty.\")\n\n predictions = model.predict(data.x_test)\n\n if isinstance(predictions, np.ndarray) and predictions.ndim > 1:\n predictions = predictions.flatten()\n\n predictions = np.clip(predictions, 1.0, 29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14913210699585525} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0 \n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after loading. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold, \n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found: {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found: {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n data = types.SimpleNamespace()\n\n # Drop rows where target is NaN\n initial_len = len(train_df)\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n if len(train_df) < initial_len:\n print(f\"Dropped {initial_len - len(train_df)} rows with NaN target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n # Define columns to drop\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n # Preserve IDs and drop from test\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} missing in test set.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Feature Engineering: Ensure consistent column sets\n for col in data.x_train.columns:\n if col not in data.x_test.columns:\n data.x_test[col] = 0\n\n # Reorder test columns to match train\n data.x_test = data.x_test[data.x_train.columns]\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Training and test columns are not aligned.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not numerical_features and not categorical_features:\n raise ValueError(\"No features found for preprocessing.\")\n\n num_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n cat_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', num_transformer, numerical_features),\n ('cat', cat_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500,\n early_stopping=True,\n scoring='neg_mean_squared_log_error'\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 1.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object missing x_test.\")\n\n predictions = model.predict(data.x_test)\n\n # Rings must be positive for RMSLE and logical consistency\n predictions = np.clip(predictions, 0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14884559701938946} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model_raw = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model_raw)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n MIN_RINGS_PREDICTION=1, \n MAX_RINGS_PREDICTION=29, \n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n verbose=-1 \n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300], \n 'model__learning_rate': [0.05, 0.1], \n 'model__num_leaves': [20, 31], \n 'model__max_depth': [5, 7], \n 'model__reg_alpha': [0.1, 0.5], \n 'model__reg_lambda': [0.1, 0.5], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom lightgbm import LGBMRegressor\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n try:\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure data files are located at {config.INPUT_DIR}. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n missing_cols = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_cols:\n data.x_test[col] = 0\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data.x_test is required for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 1, 30)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14788453909821336} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column is not numeric after post-processing.\")\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n verbose=-1 \n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300], \n 'model__learning_rate': [0.05, 0.1], \n 'model__num_leaves': [20, 31], \n 'model__max_depth': [5, 7], \n 'model__reg_alpha': [0.1, 0.5], \n 'model__reg_lambda': [0.1, 0.5], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0 \n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after loading. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df_no_id, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df_no_id[config.TARGET_COLUMN].copy()\n\n data.x_train = train_df_no_id.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"preprocessor\", preprocessor), (\"model\", model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.15115861992247548} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport lightgbm as lgb\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found: {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found: {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Handle NaNs in Target\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n def engineer_features(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n # The S4E4 competition uses Whole weight.1 for Shucked weight and Whole weight.2 for Viscera weight\n rename_map = {\n 'Whole weight.1': 'Shucked weight',\n 'Whole weight.2': 'Viscera weight',\n 'Shell weight.1': 'Shell weight'\n }\n df = df.rename(columns=rename_map)\n\n # Common Abalone Features\n if all(c in df.columns for c in ['Length', 'Diameter', 'Height']):\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Surface_Area'] = 2 * (df['Length'] * df['Diameter'] + df['Diameter'] * df['Height'] + df['Height'] * df['Length'])\n\n if 'Shucked weight' in df.columns and 'Whole weight' in df.columns:\n df['Weight_Ratio'] = df['Shucked weight'] / (df['Whole weight'] + 1e-9)\n\n if 'Shell weight' in df.columns and 'Whole weight' in df.columns:\n df['Shell_Ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n\n return df\n\n train_df = engineer_features(train_df)\n test_df = engineer_features(test_df)\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n # Drop non-feature columns\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n # Preserve IDs\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} not found in test data\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns explicitly\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=0)\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n # Abalone competition is evaluated with RMSLE. Using log transformation on target\n # is a common and effective approach for RMSLE optimization.\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbosity=-1,\n importance_type='gain'\n )\n return sklearn.compose.TransformedTargetRegressor(\n regressor=lgbm,\n func=np.log1p,\n inverse_func=np.expm1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [500, 1000],\n 'model__regressor__learning_rate': [0.05, 0.1],\n 'model__regressor__num_leaves': [31, 63],\n 'model__regressor__max_depth': [-1, 12]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Since we use TransformedTargetRegressor(log1p), minimizing MSE on the log-target\n # is equivalent to minimizing MSLE on the original target.\n # However, to use the provided y_train (original Rings) in GridSearchCV,\n # we use 'neg_mean_squared_log_error'.\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object must have x_test\")\n\n predictions = model.predict(data.x_test)\n\n # Post-processing: Target Rings is an integer age. \n # TransformedTargetRegressor already applied expm1. We clip to valid ranges.\n predictions = np.clip(predictions, 1, 30)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14730107081570123} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan \n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy() \n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model_obj = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model_obj)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14778357751251783} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy() \n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n print(submission.head())\n except Exception as e:\n pass", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"preprocessor\", preprocessor), (\"model\", model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan \n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[], \n HEIGHT_ZERO_REPLACE_NAN=True, \n PREDICTION_MIN=1, \n PREDICTION_MAX=29, \n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy() \n\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan \n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n objective='regression_l2', \n verbose=-1 \n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data = types.SimpleNamespace()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]).values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n preds_log = model.predict(data.x_test)\n if hasattr(preds_log, 'reshape'):\n preds_log = preds_log.reshape(-1)\n predictions = np.expm1(preds_log)\n predictions = np.clip(predictions, 0.5, 29.5)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14699813348668336} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n verbose=-1 \n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300], \n 'model__learning_rate': [0.05, 0.1], \n 'model__num_leaves': [20, 31], \n 'model__max_depth': [5, 7], \n 'model__reg_alpha': [0.1, 0.5], \n 'model__reg_lambda': [0.1, 0.5], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n if hasattr(predictions, 'reshape'):\n predictions = predictions.reshape(-1)\n\n predictions = np.clip(predictions, 0.5, 29.5)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14807367443519404} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n if config_obj.INPUT_DIR.exists():\n data_obj = load_data(config_obj)\n proc = get_preprocessor(config_obj, data_obj)\n reg = get_model(config_obj)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', proc), ('model', reg)])\n full_pipeline.fit(data_obj.x_train, data_obj.y_train)\n sub_df = get_submission(full_pipeline, data_obj, config_obj)\n sub_df.to_csv(\"submission.csv\", index=False)", "y": 0.1492298511262347} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef engineer_features(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n return df\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n train_df = engineer_features(train_df)\n test_df = engineer_features(test_df)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n if hasattr(predictions, 'reshape'):\n predictions = predictions.reshape(-1)\n predictions = np.clip(predictions, 0.5, 29.5)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14807367443519404} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if config.INPUT_DIR.exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14759149323051432} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef engineer_features(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n return df\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n train_df = engineer_features(train_df)\n test_df = engineer_features(test_df)\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n if hasattr(predictions, 'reshape'):\n predictions = predictions.reshape(-1)\n\n predictions = np.clip(predictions, 0.5, 29.5)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14807367443519404} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14655067429382065} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5, \n MIN_RINGS_PREDICTION=1, \n MAX_RINGS_PREDICTION=29, \n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n verbose=-1 \n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300], \n 'model__learning_rate': [0.05, 0.1], \n 'model__num_leaves': [20, 31], \n 'model__max_depth': [5, 7], \n 'model__reg_alpha': [0.1, 0.5], \n 'model__reg_lambda': [0.1, 0.5], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n if hasattr(predictions, 'reshape'):\n predictions = predictions.reshape(-1)\n predictions = np.clip(predictions, 0.5, 29.5)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14807367443519404} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if config.INPUT_DIR.exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n \"\"\"\n Define ALL constants and configuration flags for the script.\n \"\"\"\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Load data, separate target, and align columns.\n \"\"\"\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n \"\"\"\n Define and return a ColumnTransformer for data preprocessing.\n \"\"\"\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n \"\"\"\n Define and return an instantiated, Scikit-Learn compatible XGBoost model.\n \"\"\"\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n \"\"\"\n Specify a hyperparameter grid for GridSearchCV.\n \"\"\"\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n \"\"\"\n Custom RMSLE scorer function for sklearn.metrics.make_scorer.\n \"\"\"\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric.\n \"\"\"\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n \"\"\"\n Define and return an instantiated cross-validation splitter.\n \"\"\"\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n \"\"\"\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14932339319726443} +{"x": "s4e4\nimport os\nimport types\nimport pathlib\nimport json\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport sklearn.model_selection\nimport sklearn.metrics\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable, Optional\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n ps_rename = {\n \"Whole weight.1\": \"Shucked weight\",\n \"Whole weight.2\": \"Viscera weight\"\n }\n train_df = train_df.rename(columns=ps_rename)\n test_df = test_df.rename(columns=ps_rename)\n\n train_df = train_df.loc[:, ~train_df.columns.duplicated()]\n test_df = test_df.loc[:, ~test_df.columns.duplicated()]\n\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_base = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_base if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n data.x_test = data.x_test.reindex(columns=data.x_train.columns, fill_value=np.nan)\n\n if data.x_train.shape[1] != data.x_test.shape[1]:\n raise ValueError(f\"Feature mismatch: train {data.x_train.shape[1]}, test {data.x_test.shape[1]}\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__num_leaves': [31, 63],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [-1, 12],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n preds = model.predict(data.x_test)\n if hasattr(preds, 'reshape'):\n preds = preds.reshape(-1)\n\n predictions = preds\n predictions = np.clip(predictions, 0.5, 29.5)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14807367443519404} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0 \n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after loading. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14778357751251783} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = [\"Sex\"]\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if config.INPUT_DIR.exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n if config_obj.INPUT_DIR.exists():\n data_obj = load_data(config_obj)\n proc = get_preprocessor(config_obj, data_obj)\n reg = get_model(config_obj)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', proc), ('model', reg)])\n full_pipeline.fit(data_obj.x_train, data_obj.y_train)\n sub_df = get_submission(full_pipeline, data_obj, config_obj)\n sub_df.to_csv(\"submission.csv\", index=False)", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', 'passthrough', numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n if config_obj.INPUT_DIR.exists():\n data_obj = load_data(config_obj)\n proc = get_preprocessor(config_obj, data_obj)\n reg = get_model(config_obj)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', proc), ('model', reg)])\n full_pipeline.fit(data_obj.x_train, data_obj.y_train)\n sub_df = sub_df = get_submission(full_pipeline, data_obj, config_obj)\n sub_df.to_csv(\"submission.csv\", index=False)", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after loading. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0 \n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after loading. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', 'passthrough', numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n conf = get_config()\n try:\n d = load_data(conf)\n p = get_preprocessor(conf, d)\n m = get_model(conf)\n pipe = sklearn.pipeline.Pipeline(steps=[('preprocessor', p), ('model', m)])\n pipe.fit(d.x_train, d.y_train)\n s = get_submission(pipe, d, conf)\n print(s.head())\n except Exception:\n pass", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1638192504568697} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1638192504568697} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__reg_alpha': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14953162904499348} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489900466550185} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[],\n INTERACTION_FEATURES=['Shell_weight', 'Diameter', 'Length', 'Whole_weight', 'Shucked_weight']\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n for col_name in config.INTERACTION_FEATURES:\n if col_name in df_fe.columns:\n if f'Sex_I' in df_fe.columns:\n df_fe[f'Sex_I_x_{col_name}'] = df_fe['Sex_I'] * df_fe[col_name]\n if f'Sex_M' in df_fe.columns:\n df_fe[f'Sex_M_x_{col_name}'] = df_fe['Sex_M'] * df_fe[col_name]\n if f'Sex_F' in df_fe.columns:\n df_fe[f'Sex_F_x_{col_name}'] = df_fe['Sex_F'] * df_fe[col_name]\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15010922061979595} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[],\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1499856624040198} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1499856624040198} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test).flatten()\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482733183174271} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n if 'Height' in data.x_train.columns:\n data.x_train.loc[data.x_train['Height'] == 0, 'Height'] = np.nan\n if 'Height' in data.x_test.columns:\n data.x_test.loc[data.x_test['Height'] == 0, 'Height'] = np.nan\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14906103261619333} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n full_p = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n full_p.fit(dt.x_train, dt.y_train)\n sub_df = get_submission(full_p, dt, cfg)\n sub_df.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n if 'Height' in data.x_train.columns:\n data.x_train.loc[data.x_train['Height'] == 0, 'Height'] = np.nan\n if 'Height' in data.x_test.columns:\n data.x_test.loc[data.x_test['Height'] == 0, 'Height'] = np.nan\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14906103261619333} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__reg_alpha': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15040325696817872} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return model\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__reg_alpha': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15040325696817872} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14967039514163435} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[],\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14967039514163435} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[],\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14967039514163435} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943076153917081} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformations.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634024615937277} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14967039514163435} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1493289369644033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943076153917081} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14883882951371868} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\nclass StackingRegressor(BaseEstimator, RegressorMixin):\n def __init__(self, base_models: List[Model], meta_model: Model, n_splits: int = 5, random_state: int = 42):\n if not isinstance(base_models, list) or not all(isinstance(m, BaseEstimator) for m in base_models):\n raise TypeError(\"base_models must be a list of scikit-learn estimators.\")\n if not isinstance(meta_model, BaseEstimator):\n raise TypeError(\"meta_model must be a scikit-learn estimator.\")\n if not isinstance(n_splits, int) or n_splits < 2:\n raise ValueError(\"n_splits must be an integer >= 2.\")\n if not isinstance(random_state, int):\n raise ValueError(\"random_state must be an integer.\")\n\n self.base_models = base_models\n self.meta_model = meta_model\n self.n_splits = n_splits\n self.random_state = random_state\n\n self.fitted_base_models_full_data_ = None\n self.meta_model_ = None\n\n def fit(self, X: pd.DataFrame, y: pd.Series):\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if not isinstance(y, (pd.Series, np.ndarray)):\n raise TypeError(\"y must be a pandas Series or numpy array.\")\n if X.shape[0] != y.shape[0]:\n raise ValueError(\"X and y must have the same number of samples.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n y_arr = y.to_numpy() if isinstance(y, pd.Series) else y\n\n n_samples = X_arr.shape[0]\n n_base_models = len(self.base_models)\n\n oof_predictions = np.zeros((n_samples, n_base_models))\n\n kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state)\n\n for i, (train_idx, val_idx) in enumerate(kf.split(X_arr, y_arr)):\n X_train_fold, y_train_fold = X_arr[train_idx], y_arr[train_idx]\n X_val_fold = X_arr[val_idx]\n\n for j, base_model_orig in enumerate(self.base_models):\n model = clone(base_model_orig)\n model.fit(X_train_fold, y_train_fold)\n oof_predictions[val_idx, j] = model.predict(X_val_fold)\n\n self.meta_model_ = clone(self.meta_model)\n self.meta_model_.fit(oof_predictions, y_arr)\n\n self.fitted_base_models_full_data_ = []\n for base_model_orig in self.base_models:\n model = clone(base_model_orig)\n model.fit(X_arr, y_arr)\n self.fitted_base_models_full_data_.append(model)\n\n return self\n\n def predict(self, X: pd.DataFrame) -> np.ndarray:\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if self.fitted_base_models_full_data_ is None or self.meta_model_ is None:\n raise RuntimeError(\"StackingRegressor not fitted. Call fit() first.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n\n n_test_samples = X_arr.shape[0]\n n_base_models = len(self.fitted_base_models_full_data_)\n\n base_test_predictions = np.zeros((n_test_samples, n_base_models))\n for j, model in enumerate(self.fitted_base_models_full_data_):\n base_test_predictions[:, j] = model.predict(X_arr)\n\n final_predictions = self.meta_model_.predict(base_test_predictions)\n final_predictions = np.maximum(0, final_predictions)\n\n return final_predictions\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n xgbr = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0,\n lambda_=1,\n alpha=0,\n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle'\n )\n\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n base_models = [lgbm, xgbr, hgbm]\n meta_model = Ridge(random_state=config.RANDOM_STATE)\n\n stacking_regressor = StackingRegressor(\n base_models=base_models,\n meta_model=meta_model,\n n_splits=config.N_STACKING_FOLDS,\n random_state=config.RANDOM_STATE\n )\n return stacking_regressor\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__meta_model__alpha': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14956252429587946} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n NUDGE_THRESHOLD_LOW=27.5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n predictions = np.where(\n (predictions >= config.NUDGE_THRESHOLD_LOW) & (predictions < config.TARGET_MAX),\n config.TARGET_MAX,\n predictions\n )\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import TransformedTargetRegressor\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\n model = TransformedTargetRegressor(\n regressor=lgbm_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14779170349733076} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n N_FEATURES_TO_SELECT=20,\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1476466239009968} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1476466239009968} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n N_FEATURES_TO_SELECT=20,\n\n FEATURE_GEN_OPS_UNARY=[np.log1p, np.sqrt, np.square],\n FEATURE_GEN_OPS_BINARY=['+', '-', '*', '/'],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n numerical_cols = [col for col in combined_train_df.select_dtypes(include=np.number).columns.tolist() if col != config.TARGET_COLUMN]\n\n def generate_features(df: pd.DataFrame, numerical_features: List[str], config: types.SimpleNamespace) -> pd.DataFrame:\n df_copy = df.copy()\n for col in numerical_features:\n if config.FEATURE_GEN_OPS_UNARY:\n for op in config.FEATURE_GEN_OPS_UNARY:\n if op == np.log1p:\n df_copy[f'{col}_log1p'] = np.log1p(df_copy[col].fillna(0).astype(float))\n elif op == np.sqrt:\n df_copy[f'{col}_sqrt'] = np.sqrt(df_copy[col].fillna(0).astype(float).clip(lower=0))\n elif op == np.square:\n df_copy[f'{col}_square'] = np.square(df_copy[col].astype(float))\n\n for i in range(len(numerical_features)):\n for j in range(i + 1, len(numerical_features)):\n col1 = numerical_features[i]\n col2 = numerical_features[j]\n if config.FEATURE_GEN_OPS_BINARY:\n for op_str in config.FEATURE_GEN_OPS_BINARY:\n try:\n if op_str == '+':\n df_copy[f'{col1}_plus_{col2}'] = df_copy[col1] + df_copy[col2]\n elif op_str == '-':\n df_copy[f'{col1}_minus_{col2}'] = df_copy[col1] - df_copy[col2]\n elif op_str == '*':\n df_copy[f'{col1}_times_{col2}'] = df_copy[col1] * df_copy[col2]\n elif op_str == '/':\n with np.errstate(divide='ignore', invalid='ignore'):\n df_copy[f'{col1}_div_{col2}'] = df_copy[col1] / df_copy[col2].replace(0, np.nan)\n except Exception:\n pass\n return df_copy\n\n combined_train_df = generate_features(combined_train_df, numerical_cols, config)\n test_df = generate_features(test_df, numerical_cols, config)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14769567909880177} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n pipe = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n pipe.fit(dt.x_train, dt.y_train)\n sub = get_submission(pipe, dt, cfg)\n except Exception:\n pass", "y": 0.1476466239009968} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n sub_weight_cols = [\"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"]\n if all(col in combined_df.columns for col in sub_weight_cols + [\"Whole_weight\"]):\n temp_sum_sub_weights = combined_df[sub_weight_cols].fillna(0).sum(axis=1)\n mask_whole_weight_too_small = combined_df['Whole_weight'] < temp_sum_sub_weights\n combined_df.loc[mask_whole_weight_too_small, 'Whole_weight'] = \\\n temp_sum_sub_weights.loc[mask_whole_weight_too_small]\n for sub_col in sub_weight_cols:\n mask_sub_weight_too_large = combined_df[sub_col] > combined_df['Whole_weight']\n combined_df.loc[mask_sub_weight_too_large, sub_col] = \\\n combined_df.loc[mask_sub_weight_too_large, 'Whole_weight']\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n pipe = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n pipe.fit(dt.x_train, dt.y_train)\n sub = get_submission(pipe, dt, cfg)\n except Exception:\n pass", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n FEATURE_GEN_OPS_UNARY=[np.log1p, np.sqrt, np.square],\n FEATURE_GEN_OPS_BINARY=['+', '-', '*', '/'],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n numerical_cols = [col for col in combined_train_df.select_dtypes(include=np.number).columns.tolist() if col != config.TARGET_COLUMN]\n\n def generate_features(df: pd.DataFrame, numerical_features: List[str], config: types.SimpleNamespace) -> pd.DataFrame:\n df_copy = df.copy()\n for col in numerical_features:\n if config.FEATURE_GEN_OPS_UNARY:\n for op in config.FEATURE_GEN_OPS_UNARY:\n if op == np.log1p:\n df_copy[f'{col}_log1p'] = np.log1p(df_copy[col].fillna(0).astype(float))\n elif op == np.sqrt:\n df_copy[f'{col}_sqrt'] = np.sqrt(df_copy[col].fillna(0).astype(float).clip(lower=0))\n elif op == np.square:\n df_copy[f'{col}_square'] = np.square(df_copy[col].astype(float))\n\n for i in range(len(numerical_features)):\n for j in range(i + 1, len(numerical_features)):\n col1 = numerical_features[i]\n col2 = numerical_features[j]\n if config.FEATURE_GEN_OPS_BINARY:\n for op_str in config.FEATURE_GEN_OPS_BINARY:\n try:\n if op_str == '+':\n df_copy[f'{col1}_plus_{col2}'] = df_copy[col1] + df_copy[col2]\n elif op_str == '-':\n df_copy[f'{col1}_minus_{col2}'] = df_copy[col1] - df_copy[col2]\n elif op_str == '*':\n df_copy[f'{col1}_times_{col2}'] = df_copy[col1] * df_copy[col2]\n elif op_str == '/':\n with np.errstate(divide='ignore', invalid='ignore'):\n df_copy[f'{col1}_div_{col2}'] = df_copy[col1] / df_copy[col2].replace(0, np.nan)\n except Exception:\n pass\n return df_copy\n\n combined_train_df = generate_features(combined_train_df, numerical_cols, config)\n test_df = generate_features(test_df, numerical_cols, config)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14769567909880177} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n columns_to_check_for_zeros = [\n 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight'\n ]\n for df in [data.x_train, data.x_test]:\n for col in columns_to_check_for_zeros:\n if col in df.columns:\n df[col] = df[col].replace(0, np.nan)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14773869753087515} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1494324566751729} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n pipe = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n pipe.fit(dt.x_train, dt.y_train)\n sub = get_submission(pipe, dt, cfg)\n except Exception:\n pass", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15076642816010863} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n pipe = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n pipe.fit(dt.x_train, dt.y_train)\n sub = get_submission(pipe, dt, cfg)\n except Exception:\n pass", "y": 0.14771915867885926} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\nScorerString = str\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory {config.INPUT_DIR} does not exist.\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file {train_path} not found.\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file {test_path} not found.\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN]).reset_index(drop=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} missing from test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n missing_cols = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_cols:\n data.x_test[col] = 0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Mismatch in columns between train and test after alignment.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n loss='squared_error',\n max_iter=1000,\n early_stopping=True,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data.x_test is required for submission.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.148481383608811} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline([\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n print(submission.head())\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1492573452665279} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n pipe = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n pipe.fit(dt.x_train, dt.y_train)\n sub = get_submission(pipe, dt, cfg)\n except Exception:\n pass", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14655067429382065} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline([\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train. This indicates an unexpected data structure.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data or is not categorical/object type.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n full_p = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n full_p.fit(dt.x_train, dt.y_train)\n sub_df = get_submission(full_p, dt, cfg)\n sub_df.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n full_p = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n full_p.fit(dt.x_train, dt.y_train)\n sub_df = get_submission(full_p, dt, cfg)\n sub_df.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_train.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1496624778589223} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1496624778589223} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__reg_alpha': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15147418737606974} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__reg_alpha': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15147418737606974} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return model\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__reg_alpha': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15147418737606974} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return model\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__reg_alpha': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1501951336654112} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n mdl = get_model(cfg)\n pipe = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', mdl)])\n pipe.fit(dt.x_train, dt.y_train)\n sub = get_submission(pipe, dt, cfg)\n except Exception:\n pass", "y": 0.14655067429382065} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15059055426385912} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(\"Columns present in test but not in train.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"preprocessor\", preprocessor), (\"model\", model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15059055426385912} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[],\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15059055426385912} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14883882951371868} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14655067429382065} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\nScorerString = str\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory {config.INPUT_DIR} does not exist.\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file {train_path} not found.\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file {test_path} not found.\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n data = types.SimpleNamespace()\n\n # Handle NaNs in Target\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN]).reset_index(drop=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n # Preserve Test IDs\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} missing from test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Feature Engineering: Basic ratios can help with Abalone data\n def add_features(df):\n df = df.copy()\n # Avoid division by zero with small epsilon\n df['volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['density'] = df['Whole weight'] / (df['volume'] + 1e-8)\n return df\n\n train_df = add_features(train_df)\n test_df = add_features(test_df)\n\n # Drop columns robustly\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align Columns\n missing_cols = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_cols:\n data.x_test[col] = 0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Mismatch in columns between train and test after alignment.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n loss='squared_error',\n max_iter=1000,\n early_stopping=True,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Competition metric is RMSLE\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data.x_test is required for submission.\")\n\n predictions = model.predict(data.x_test)\n\n # Rings cannot be negative for MSLE calculation and real world\n predictions = np.clip(predictions, 0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14871090891084537} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15059055426385912} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[],\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15059055426385912} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n )\n return model\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15059055426385912} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformations.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[],\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return hgbm\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__l2_regularization': [0.1, 1.0, 10.0],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14967039514163435} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\nos.environ['OPENBLAS_VERBOSE'] = '0'\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\nScorerString = str\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply preprocessing.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan \n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical preprocessing.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists() or not test_path.exists():\n raise FileNotFoundError(f\"Data files not found in {config.INPUT_DIR}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Remove rows with NaN in target\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n\n # Feature Engineering\n for df in [train_df, test_df]:\n df['volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['weight_sum'] = df['Whole weight.1'] + df['Whole weight.2'] + df['Shell weight']\n df['weight_ratio'] = df['Whole weight.1'] / (df['Whole weight'] + 1e-9)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Column alignment\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=500,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=15,\n categorical_features=None # Handled by preprocessor OneHotEncoder\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10],\n 'model__max_leaf_nodes': [31, 63]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(model, 'predict'):\n raise TypeError(\"The provided model does not have a predict method.\")\n\n predictions = model.predict(data.x_test)\n\n # Abalone rings are counts, strictly positive. Clip to avoid metric issues and ensure logic.\n predictions = np.clip(predictions, 1.0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14882998810885217} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14778357751251783} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = [\"Sex\"]\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n N_FEATURES_TO_SELECT=20,\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n try:\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure data files are located at {config.INPUT_DIR}. Error: {e}\")\n\n # Remove target NaNs\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n # Feature Engineering\n for df in [train_df, test_df]:\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Weight_Sum'] = df['Whole weight'] + df['Whole weight.1'] + df['Whole weight.2'] + df['Shell weight']\n df['Density'] = df['Whole weight'] / (df['Volume'] + 1e-9)\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n\n # Store test IDs before dropping\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Ensure column alignment\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=1000,\n early_stopping=True,\n validation_fraction=0.1,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_iter': [100, 300],\n 'model__max_depth': [10, 15],\n 'model__l2_regularization': [0.0, 0.1]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n # Rings must be positive, clipping to a safe range\n predictions = np.clip(predictions, 1, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14959099956824365} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n N_FEATURES_TO_SELECT=20,\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n N_FEATURES_TO_SELECT=20,\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import KFold, ParameterGrid\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\nScorerString = str\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not config.INPUT_DIR.exists():\n raise FileNotFoundError(f\"Input directory {config.INPUT_DIR} does not exist.\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file {train_path} not found.\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file {test_path} not found.\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN]).reset_index(drop=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column {config.ID_COLUMN} missing from test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n missing_cols = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_cols:\n data.x_test[col] = 0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Mismatch in columns between train and test after alignment.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n loss='squared_error',\n max_iter=1000,\n early_stopping=True,\n n_iter_no_change=20\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.05, 0.1],\n 'model__max_depth': [5, 10, None],\n 'model__l2_regularization': [0.0, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data.x_test is required for submission.\")\n\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, 0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15019844718581024} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline([\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline([\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error, mean_squared_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n\n if y_true_clean.size == 0:\n return np.nan\n\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n\n HEIGHT_ZERO_REPLACE_NAN=True,\n\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_estimator = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n\n return lgbm_estimator\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = [\"Sex\"]\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_train.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n train_df = pd.DataFrame({\n config.ID_COLUMN: range(10),\n config.TARGET_COLUMN: np.random.randint(1, 30, 10),\n \"feat1\": np.random.rand(10),\n \"cat1\": [\"A\"] * 10\n })\n test_df = pd.DataFrame({\n config.ID_COLUMN: range(10, 15),\n \"feat1\": np.random.rand(5),\n \"cat1\": [\"A\"] * 5\n })\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n regressor = get_model(config)\n model = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', regressor)\n ])\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\n\nif __name__ == \"__main__\":\n try:\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport lightgbm as lgb\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Testing file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Fail fast: ensure target is in training data\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in training data.\")\n\n # Drop NaNs in target\n initial_len = len(train_df)\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n if len(train_df) < initial_len:\n print(f\"Dropped {initial_len - len(train_df)} rows with NaN target.\")\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Feature engineering: Volume and Weight Ratios\n def add_features(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Weight_Ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n return df\n\n train_df = add_features(train_df)\n test_df = add_features(test_df)\n\n # Preserve IDs\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Drop unnecessary columns\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n if not hasattr(data, 'x_train') or not isinstance(data.x_train, pd.DataFrame):\n raise AttributeError(\"data.x_train must be a pandas.DataFrame\")\n\n numeric_features = data.x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = data.x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numeric_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numeric_transformer, numeric_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n # Intelligent grid targeting ~12 combinations\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.03, 0.05],\n 'model__num_leaves': [31, 63, 127],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # RMSLE is the standard for Abalone; scikit-learn uses negative MSLE\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n if not hasattr(config, 'N_SPLITS'):\n raise AttributeError(\"config.N_SPLITS is required\")\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data.x_test is required for submission\")\n\n predictions = model.predict(data.x_test)\n\n # Rings must be positive; clip to known range [1, 29]\n predictions = np.clip(predictions, 1, 29)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14827370382119828} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n train_df = pd.DataFrame({\n config.ID_COLUMN: range(10),\n config.TARGET_COLUMN: np.random.randint(1, 30, 10),\n \"feat1\": np.random.rand(10),\n \"cat1\": [\"A\"] * 10\n })\n test_df = pd.DataFrame({\n config.ID_COLUMN: range(10, 15),\n \"feat1\": np.random.rand(5),\n \"cat1\": [\"A\"] * 5\n })\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)", "y": 0.14943173465602355} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14778357751251783} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14778357751251783} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14778357751251783} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass RegressorModel(Model, Protocol):\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n train_df = pd.DataFrame({\n config.ID_COLUMN: range(10),\n config.TARGET_COLUMN: np.random.randint(1, 30, 10),\n \"feat1\": np.random.rand(10),\n \"cat1\": [\"A\"] * 10\n })\n test_df = pd.DataFrame({\n config.ID_COLUMN: range(10, 15),\n \"feat1\": np.random.rand(5),\n \"cat1\": [\"A\"] * 5\n })\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> RegressorModel:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1478762762664878} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', 'passthrough', numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None:\n pass\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any:\n pass\n def set_params(self, **kwargs: Any) -> None:\n pass\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None:\n pass\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any:\n pass\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any:\n pass\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int:\n pass\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]:\n pass\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n pass\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as error:\n raise FileNotFoundError(f\"Ensure input directory and files exist. Error: {error}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with missing target values.\")\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[column for column in cols_to_drop_train if column in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[column for column in cols_to_drop_test if column in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for column in missing_in_test:\n data.x_test[column] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected model to be an instance of Model protocol, got {type(model)}\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n configuration = get_config()\n try:\n dataset = load_data(configuration)\n data_preprocessor = get_preprocessor(configuration, dataset)\n base_regressor = get_model(configuration)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', data_preprocessor),\n ('model', base_regressor)\n ])\n full_pipeline.fit(dataset.x_train, dataset.y_train)\n final_submission = get_submission(full_pipeline, dataset, configuration)\n print(final_submission.head())\n except Exception as execution_error:\n print(f\"Workflow failed: {execution_error}\")", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', 'passthrough', numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n return base_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1634145036204615} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p, \n inverse_func=np.expm1 \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1638192504568697} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer, TransformedTargetRegressor\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n if pd.api.types.is_numeric_dtype(df['Height']):\n df['Height'] = df['Height'].replace(0, np.nan)\n else:\n raise TypeError(f\"Column 'Height' is not numeric in one of the dataframes. Type: {df['Height'].dtype}\")\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n model = TransformedTargetRegressor(\n regressor=base_model,\n func=np.log1p, \n inverse_func=np.expm1 \n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1638192504568697} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport sklearn\nfrom sklearn.model_selection import KFold, StratifiedKFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({})\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14953162904499348} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489900466550185} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487695205974899} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n INTERACTION_FEATURES=['Shell_weight', 'Diameter', 'Length', 'Whole_weight', 'Shucked_weight']\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Height' in df_fe.columns:\n df_fe['Height'] = df_fe['Height'].replace(0, np.nan) \n else:\n df_fe['Height'] = np.nan\n\n epsilon = np.finfo(float).eps\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['crab_area'] = df_fe['Length'] * df_fe['Diameter']\n else:\n df_fe['crab_area'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'crab_area' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['approx_density'] = df_fe['Whole_weight'] / (df_fe['crab_area'].replace(0, epsilon) * df_fe['Height'].fillna(1).replace(0, epsilon))\n else:\n df_fe['approx_density'] = np.nan\n\n if 'Whole_weight' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['bmi'] = df_fe['Whole_weight'] / (df_fe['Height'].fillna(1).replace(0, epsilon)**2)\n else:\n df_fe['bmi'] = np.nan\n\n if 'Shucked_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['meat_ratio'] = df_fe['Shucked_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['meat_ratio'] = np.nan\n\n if 'Shell_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['shell_ratio'] = df_fe['Shell_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['shell_ratio'] = np.nan\n\n if 'Viscera_weight' in df_fe.columns and 'Whole_weight' in df_fe.columns:\n df_fe['viscera_ratio'] = df_fe['Viscera_weight'] / df_fe['Whole_weight'].replace(0, epsilon)\n else:\n df_fe['viscera_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Diameter' in df_fe.columns:\n df_fe['length_dia_ratio'] = df_fe['Length'] / df_fe['Diameter'].replace(0, epsilon)\n else:\n df_fe['length_dia_ratio'] = np.nan\n\n if 'Length' in df_fe.columns and 'Height' in df_fe.columns:\n df_fe['length_height_ratio'] = df_fe['Length'] / df_fe['Height'].fillna(1).replace(0, epsilon)\n else:\n df_fe['length_height_ratio'] = np.nan\n\n required_water_loss_cols = ['Whole_weight', 'Shucked_weight', 'Viscera_weight', 'Shell_weight']\n if all(col in df_fe.columns for col in required_water_loss_cols):\n df_fe['water_loss'] = df_fe['Whole_weight'] - df_fe['Shucked_weight'] - df_fe['Viscera_weight'] - df_fe['Shell_weight']\n else:\n df_fe['water_loss'] = np.nan\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n for col_name in config.INTERACTION_FEATURES:\n if col_name in df_fe.columns:\n if f'Sex_I' in df_fe.columns:\n df_fe[f'Sex_I_x_{col_name}'] = df_fe['Sex_I'] * df_fe[col_name]\n if f'Sex_M' in df_fe.columns:\n df_fe[f'Sex_M_x_{col_name}'] = df_fe['Sex_M'] * df_fe[col_name]\n if f'Sex_F' in df_fe.columns:\n df_fe[f'Sex_F_x_{col_name}'] = df_fe['Sex_F'] * df_fe[col_name]\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14845964941814957} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Protocol, Union, List, Iterator, Tuple, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17649568467993004} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n if 'Height' in data.x_train.columns:\n data.x_train.loc[data.x_train['Height'] == 0, 'Height'] = np.nan\n if 'Height' in data.x_test.columns:\n data.x_test.loc[data.x_test['Height'] == 0, 'Height'] = np.nan\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14906103261619333} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Protocol, Union, List, Iterator, Tuple, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n FEATURE_GEN_OPS_UNARY=[np.log1p, np.sqrt, np.square],\n FEATURE_GEN_OPS_BINARY=['+', '-', '*', '/'],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n numerical_cols = [col for col in combined_train_df.select_dtypes(include=np.number).columns.tolist() if col != config.TARGET_COLUMN]\n def generate_features(df: pd.DataFrame, numerical_features: List[str], config: types.SimpleNamespace) -> pd.DataFrame:\n df_copy = df.copy()\n for col in numerical_features:\n if config.FEATURE_GEN_OPS_UNARY:\n for op in config.FEATURE_GEN_OPS_UNARY:\n if op == np.log1p:\n df_copy[f'{col}_log1p'] = np.log1p(df_copy[col].fillna(0).astype(float))\n elif op == np.sqrt:\n df_copy[f'{col}_sqrt'] = np.sqrt(df_copy[col].fillna(0).astype(float).clip(lower=0))\n elif op == np.square:\n df_copy[f'{col}_square'] = np.square(df_copy[col].astype(float))\n for i in range(len(numerical_features)):\n for j in range(i + 1, len(numerical_features)):\n col1 = numerical_features[i]\n col2 = numerical_features[j]\n if config.FEATURE_GEN_OPS_BINARY:\n for op_str in config.FEATURE_GEN_OPS_BINARY:\n try:\n if op_str == '+':\n df_copy[f'{col1}_plus_{col2}'] = df_copy[col1] + df_copy[col2]\n elif op_str == '-':\n df_copy[f'{col1}_minus_{col2}'] = df_copy[col1] - df_copy[col2]\n elif op_str == '*':\n df_copy[f'{col1}_times_{col2}'] = df_copy[col1] * df_copy[col2]\n elif op_str == '/':\n with np.errstate(divide='ignore', invalid='ignore'):\n df_copy[f'{col1}_div_{col2}'] = df_copy[col1] / df_copy[col2].replace(0, np.nan)\n except Exception:\n pass\n return df_copy\n combined_train_df = generate_features(combined_train_df, numerical_cols, config)\n test_df = generate_features(test_df, numerical_cols, config)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.17694993046940088} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport sklearn\nfrom sklearn.model_selection import KFold, StratifiedKFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({})\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1503392542694313} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport sklearn\nfrom sklearn.model_selection import KFold, StratifiedKFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({})\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1503392542694313} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n try:\n data_obj = load_data(config_obj)\n preprocessor_obj = get_preprocessor(config_obj, data_obj)\n model_obj = get_model(config_obj)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor_obj),\n ('model', model_obj)\n ])\n full_pipeline.fit(data_obj.x_train, data_obj.y_train)\n submission = get_submission(full_pipeline, data_obj, config_obj)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14987959007878854} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14812928594757882} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14834265182626674} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14812928594757882} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14812928594757882} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\nclass StackingRegressor(BaseEstimator, RegressorMixin):\n def __init__(self, base_models: List[Model], meta_model: Model, n_splits: int = 5, random_state: int = 42):\n if not isinstance(base_models, list) or not all(isinstance(m, BaseEstimator) for m in base_models):\n raise TypeError(\"base_models must be a list of scikit-learn estimators.\")\n if not isinstance(meta_model, BaseEstimator):\n raise TypeError(\"meta_model must be a scikit-learn estimator.\")\n if not isinstance(n_splits, int) or n_splits < 2:\n raise ValueError(\"n_splits must be an integer >= 2.\")\n if not isinstance(random_state, int):\n raise ValueError(\"random_state must be an integer.\")\n\n self.base_models = base_models\n self.meta_model = meta_model\n self.n_splits = n_splits\n self.random_state = random_state\n self.fitted_base_models_full_data_ = None\n self.meta_model_ = None\n\n def fit(self, X: pd.DataFrame, y: pd.Series):\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if not isinstance(y, (pd.Series, np.ndarray)):\n raise TypeError(\"y must be a pandas Series or numpy array.\")\n if X.shape[0] != y.shape[0]:\n raise ValueError(\"X and y must have the same number of samples.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n y_arr = y.to_numpy() if isinstance(y, pd.Series) else y\n\n n_samples = X_arr.shape[0]\n n_base_models = len(self.base_models)\n\n oof_predictions = np.zeros((n_samples, n_base_models))\n\n kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state)\n\n for i, (train_idx, val_idx) in enumerate(kf.split(X_arr, y_arr)):\n X_train_fold, y_train_fold = X_arr[train_idx], y_arr[train_idx]\n X_val_fold = X_arr[val_idx]\n\n for j, base_model_orig in enumerate(self.base_models):\n model = clone(base_model_orig)\n model.fit(X_train_fold, y_train_fold)\n oof_predictions[val_idx, j] = model.predict(X_val_fold)\n\n self.meta_model_ = clone(self.meta_model)\n self.meta_model_.fit(oof_predictions, y_arr)\n\n self.fitted_base_models_full_data_ = []\n for base_model_orig in self.base_models:\n model = clone(base_model_orig)\n model.fit(X_arr, y_arr)\n self.fitted_base_models_full_data_.append(model)\n\n return self\n\n def predict(self, X: pd.DataFrame) -> np.ndarray:\n if not isinstance(X, (pd.DataFrame, np.ndarray)):\n raise TypeError(\"X must be a pandas DataFrame or numpy array.\")\n if self.fitted_base_models_full_data_ is None or self.meta_model_ is None:\n raise RuntimeError(\"StackingRegressor not fitted. Call fit() first.\")\n\n X_arr = X.to_numpy() if isinstance(X, pd.DataFrame) else X\n\n n_test_samples = X_arr.shape[0]\n n_base_models = len(self.fitted_base_models_full_data_)\n\n base_test_predictions = np.zeros((n_test_samples, n_base_models))\n for j, model in enumerate(self.fitted_base_models_full_data_):\n base_test_predictions[:, j] = model.predict(X_arr)\n\n final_predictions = self.meta_model_.predict(base_test_predictions)\n final_predictions = np.maximum(0, final_predictions)\n\n return final_predictions\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = TransformedTargetRegressor(\n regressor=lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n xgbr = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n max_depth=6,\n subsample=0.7,\n colsample_bytree=0.7,\n gamma=0.0, \n lambda_=1, \n alpha=0, \n n_jobs=-1,\n tree_method='hist',\n eval_metric='rmsle' \n )\n\n hgbm = TransformedTargetRegressor(\n regressor=HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=150,\n learning_rate=0.04,\n max_leaf_nodes=25,\n max_depth=7,\n l2_regularization=0.1,\n ),\n func=np.log1p,\n inverse_func=np.expm1\n )\n\n base_models = [lgbm, xgbr, hgbm]\n meta_model = Ridge(random_state=config.RANDOM_STATE)\n\n stacking_regressor = StackingRegressor(\n base_models=base_models,\n meta_model=meta_model,\n n_splits=config.N_STACKING_FOLDS,\n random_state=config.RANDOM_STATE\n )\n return stacking_regressor\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__meta_model__alpha': [0.1, 1.0, 10.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14956252429587946} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n return base_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943076153917081} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16342083005532684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1494324566751729} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14883882951371868} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1494324566751729} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14883882951371868} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.148254507105008} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import TransformedTargetRegressor\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n model = TransformedTargetRegressor(\n regressor=lgbm_model,\n func=np.log1p,\n inverse_func=np.expm1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14779170349733076} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14817684509150483} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Protocol, Union, List, Iterator, Tuple, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n ORIGINAL_DATA_DIR=pathlib.Path(\"/kaggle/input/abalone-dataset\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ORIGINAL_ABALONE_FILE=\"abalone.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n original_df = pd.read_csv(config.ORIGINAL_DATA_DIR / config.ORIGINAL_ABALONE_FILE)\n data = types.SimpleNamespace()\n original_df.columns = [\n 'Sex', 'Length', 'Diameter', 'Height', 'Whole_weight',\n 'Shucked_weight', 'Viscera_weight', 'Shell_weight', 'Rings'\n ]\n original_df[config.ID_COLUMN] = original_df.index + train_df[config.ID_COLUMN].max() + 1\n train_df_no_id = train_df.drop(columns=[config.ID_COLUMN])\n common_cols = [col for col in train_df_no_id.columns if col in original_df.columns]\n train_df_for_concat = train_df_no_id[common_cols]\n original_df_for_concat = original_df[common_cols]\n combined_train_df = pd.concat([train_df_for_concat, original_df_for_concat], ignore_index=True)\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.18284564784044893} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Protocol, Union, List, Iterator, Tuple, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14743859714807733} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n NUDGE_THRESHOLD_LOW=27.5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n predictions = np.where(\n (predictions >= config.NUDGE_THRESHOLD_LOW) & (predictions < config.TARGET_MAX),\n config.TARGET_MAX,\n predictions\n )\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Load data, separate target, drop specified columns, handle simple data issues,\n and align train/test columns.\n \"\"\"\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric.\n We use 'neg_mean_squared_error' because GridSearchCV maximizes the score.\n \"\"\"\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n Applies clipping to predictions.\n \"\"\"\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n \"\"\"\n Load data, separate target, drop specified columns, handle simple data issues,\n and align train/test columns.\n \"\"\"\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n \"\"\"\n Provide the scoring metric.\n We use 'neg_mean_squared_error' because GridSearchCV maximizes the score.\n \"\"\"\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n \"\"\"\n Create the submission DataFrame using the model's predictions on the test set.\n Applies clipping to predictions.\n \"\"\"\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14946198675631447} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n for df in [train_df, test_df]:\n if 'Height' in df.columns:\n if (df['Height'] == 0).any():\n df['Height'] = df['Height'].replace(0, np.nan)\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.148254507105008} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n data.x_train = preprocessor.fit_transform(data.x_train)\n model.fit(data.x_train, data.y_train)\n data.x_test = preprocessor.transform(data.x_test)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14655067429382065} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14655067429382065} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.1494553418245299} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1482284097588235} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n for col in config.PHYSICAL_COLS:\n if col in combined_df.columns:\n combined_df[col] = combined_df[col].replace(0, np.nan)\n combined_df[col] = combined_df[col].apply(lambda x: np.nan if x < 0 else x)\n\n sub_weight_cols = [\"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"]\n if all(col in combined_df.columns for col in sub_weight_cols + [\"Whole_weight\"]):\n temp_sum_sub_weights = combined_df[sub_weight_cols].fillna(0).sum(axis=1)\n\n mask_whole_weight_too_small = combined_df['Whole_weight'] < temp_sum_sub_weights\n\n combined_df.loc[mask_whole_weight_too_small, 'Whole_weight'] = \\\n temp_sum_sub_weights.loc[mask_whole_weight_too_small]\n\n for sub_col in sub_weight_cols:\n mask_sub_weight_too_large = combined_df[sub_col] > combined_df['Whole_weight']\n combined_df.loc[mask_sub_weight_too_large, sub_col] = \\\n combined_df.loc[mask_sub_weight_too_large, 'Whole_weight']\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14700748526867286} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1468796149440121} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n for df in [data.x_train, data.x_test]:\n if 'Height' in df.columns:\n df['Height'] = df['Height'].replace(0, np.nan)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14945345217777378} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')), \n ('scaler', sklearn.preprocessing.StandardScaler()) \n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14771915867885926} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN])\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n\n predictions = np.expm1(log_predictions)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14655067429382065} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1489772829006657} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1492573452665279} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1494324566751729} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({})\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14848384469258005} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n if 'Sex' in data.x_train.columns:\n data.x_train = data.x_train.drop(columns=['Sex'])\n if 'Sex' in data.x_test.columns:\n data.x_test = data.x_test.drop(columns=['Sex'])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15070717529053057} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if config.INPUT_DIR.exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"preprocessor\", preprocessor), (\"model\", model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__regressor__n_estimators': [100, 200],\n 'model__regressor__learning_rate': [0.01, 0.05],\n 'model__regressor__num_leaves': [20, 31, 50],\n 'model__regressor__max_depth': [-1, 7],\n 'model__regressor__reg_alpha': [0.1, 1.0],\n 'model__regressor__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1492573452665279} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Clean target\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Basic Feature Engineering\n for df in [x_train, x_test]:\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Total_Weight_to_Shell'] = df['Whole weight'] / (df['Shell weight'] + 1e-6)\n df['Water_Weight'] = df['Whole weight'] - (df['Whole weight.1'] + df['Whole weight.2'] + df['Shell weight'])\n\n # Align columns\n data.x_train = x_train\n data.x_test = x_test[x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(exclude=[np.number]).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('ohe', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist'\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0]\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n # Ensure no negative values for log error calculations\n y_pred_clipped = np.clip(y_pred, 0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14809278012194246} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = np.log1p(train_df[config.TARGET_COLUMN]) \n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n log_predictions = model.predict(data.x_test)\n predictions = np.expm1(log_predictions)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14655067429382065} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14883882951371868} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n data.x_train = preprocessor.fit_transform(data.x_train)\n model.fit(data.x_train, data.y_train)\n data.x_test = preprocessor.transform(data.x_test)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1487909498415339} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n try:\n loaded_data = load_data(config_obj)\n preprocessor_obj = get_preprocessor(config_obj, loaded_data)\n model_obj = get_model(config_obj)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"pre\", preprocessor_obj), (\"model\", model_obj)])\n pipeline.fit(loaded_data.x_train, loaded_data.y_train)\n submission = get_submission(pipeline, loaded_data, config_obj)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15025917761719212} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n if (config.INPUT_DIR / config.TRAIN_FILE).exists() and (config.INPUT_DIR / config.TEST_FILE).exists():\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model_obj = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model_obj)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n print(submission.head())", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\n# Type alias for any valid scikit-learn CV splitter instance\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training file not found: {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found: {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if train_df.empty:\n raise ValueError(\"Training data is empty.\")\n\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' missing from training data.\")\n\n # Handle NaNs in target\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Feature Engineering\n for df in [train_df, test_df]:\n # Abalone volume calculation\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n # Ratio features\n df['Whole_to_Shell_Ratio'] = df['Whole weight'] / (df['Shell weight'] + 1e-9)\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Ensure column alignment\n missing_in_test = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_in_test:\n data.x_test[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist'\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n # Parameter combinations: 2 * 2 * 2 * 2 = 16\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [6, 8],\n 'model__subsample': [0.8, 1.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids'.\")\n\n predictions = model.predict(data.x_test)\n # RMSLE metric requires non-negative predictions.\n predictions = np.clip(predictions, 0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14798859098343684} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config_obj = get_config()\n try:\n data_obj = load_data(config_obj)\n preprocessor_obj = get_preprocessor(config_obj, data_obj)\n model_obj = get_model(config_obj)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor_obj),\n ('model', model_obj)\n ])\n full_pipeline.fit(data_obj.x_train, data_obj.y_train)\n submission = get_submission(full_pipeline, data_obj, config_obj)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[\n KFold,\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14922402223559184} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n MIN_RINGS_VALUE=1,\n MAX_RINGS_VALUE=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n predictions = np.clip(predictions, config.MIN_RINGS_VALUE, config.MAX_RINGS_VALUE)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if 'Sex' not in categorical_features:\n if 'Sex' in numerical_features:\n categorical_features.append('Sex')\n numerical_features.remove('Sex')\n else:\n raise ValueError(\"The 'Sex' column was not found in the training data.\")\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1496624778589223} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLTS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1496624778589223} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n N_STACKING_FOLDS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 150, 200],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1496624778589223} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14924725437564887} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport sklearn\nfrom sklearn.model_selection import KFold, StratifiedKFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({})\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15019090795566548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.metrics\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nnp.random.seed(42)\n\nCVSplitter = Union[KFold]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float: ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if data.x_train[c].dtype == 'object' or data.x_train[c].dtype == 'category':\n data.x_test[c] = 'missing_category'\n else:\n data.x_test[c] = 0.0\n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Columns {missing_in_train} present in test but not in train.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.HistGradientBoostingRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__max_iter': [100, 200],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__min_samples_leaf': [20, 40],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test).flatten()\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.15078840257578977} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport sklearn\nfrom sklearn.model_selection import KFold, StratifiedKFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({})\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n if cfg.INPUT_DIR.exists():\n d = load_data(cfg)\n prep = get_preprocessor(cfg, d)\n m = get_model(cfg)\n pipeline = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', m)])\n pipeline.fit(d.x_train, d.y_train)\n sub = get_submission(pipeline, d, cfg)\n sub.to_csv(\"submission.csv\", index=False)", "y": 0.15019090795566548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom sklearn.pipeline import Pipeline\n\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[],\n TARGET_MIN=1,\n TARGET_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n if c in data.x_train.select_dtypes(include=np.number).columns:\n data.x_test[c] = np.nan\n else:\n data.x_test[c] = 'missing'\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_test = data.x_test.drop(columns=[c])\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm_model = lgb.LGBMRegressor(\n objective='regression_l2',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n return lgbm_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n def rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=config.TARGET_MIN, a_max=config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1488169936111721} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14894626808369082} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n def feature_engineer(df: pd.DataFrame, config: types.SimpleNamespace) -> pd.DataFrame:\n df_fe = df.copy()\n\n if 'Sex' in df_fe.columns:\n df_fe['Sex'] = df_fe['Sex'].astype('category').cat.set_categories(['M', 'F', 'I'])\n df_sex_ohe = pd.get_dummies(df_fe['Sex'], prefix='Sex', dtype=int)\n df_fe = pd.concat([df_fe, df_sex_ohe], axis=1).drop('Sex', axis=1)\n else:\n for s_cat in ['I', 'M', 'F']:\n df_fe[f'Sex_{s_cat}'] = 0\n\n return df_fe\n\n data.x_train = feature_engineer(data.x_train, config)\n data.x_test = feature_engineer(data.x_test, config)\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1490474035651646} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nfrom sklearn.compose import TransformedTargetRegressor\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14894626808369082} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14894626808369082} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nimport sklearn\nfrom sklearn.model_selection import (\n KFold,\n StratifiedKFold,\n ParameterGrid,\n)\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\n\nimport lightgbm as lgb\n\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\n\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n\n data.x_test = data.x_test[train_cols] \n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.04, 0.1],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14894626808369082} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\nimport sklearn\nfrom sklearn.model_selection import KFold, StratifiedKFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nimport sklearn.pipeline\nfrom sklearn.base import BaseEstimator, RegressorMixin, clone\nimport lightgbm as lgb\nCVSplitter = Union[KFold, StratifiedKFold]\nModel = BaseEstimator\nProcessor = sklearn.pipeline.Pipeline\nScorerString = str\ndef rmsle_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[]\n )\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN]\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n missing_in_train = set(test_cols) - set(train_cols)\n for c in missing_in_train:\n data.x_train[c] = 0 \n data.x_test = data.x_test[train_cols] \n return data\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler()) \n ])\n if categorical_features:\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n else:\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\ndef get_model(config: types.SimpleNamespace) -> Model:\n lgbm = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_estimators=150,\n learning_rate=0.04,\n num_leaves=25,\n max_depth=7,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.7,\n subsample=0.7,\n n_jobs=-1,\n verbose=-1,\n )\n return lgbm\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.01, 0.04, 0.1],\n })\ndef get_scorer_func(config: types.SimpleNamespace) -> Callable:\n return make_scorer(rmsle_np, greater_is_better=False)\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\nif __name__ == \"__main__\":\n config = get_config()\n if os.path.exists(config.INPUT_DIR):\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline([\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)", "y": 0.14894626808369082} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n return base_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Type Aliases and Protocols ---\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n# --- Required Functions ---\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in training data.\")\n\n # Drop rows where target is NaN\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Store test IDs before dropping\n if config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in test data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Identify columns to drop robustly\n drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in train_df.columns]\n drop_test = [c for c in [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', []) if c in test_df.columns]\n\n x_train = train_df.drop(columns=drop_train)\n x_test = test_df.drop(columns=drop_test)\n\n # Feature Engineering\n for df in [x_train, x_test]:\n # Abalone specifics: Weights and Dimensions\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Weight_diff'] = df['Whole weight'] - (df['Whole weight.1'] + df['Whole weight.2'] + df['Shell weight'])\n df['Shell_ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n\n # Align columns\n x_test = x_test[x_train.columns]\n\n data.x_train = x_train\n data.x_test = x_test\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not numerical_features and not categorical_features:\n raise ValueError(\"No valid features found in training data.\")\n\n num_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n cat_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', num_transformer, numerical_features),\n ('cat', cat_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n # Histogram-based Gradient Boosting is robust and fast for tabular data\n return sklearn.ensemble.HistGradientBoostingRegressor(\n random_state=config.RANDOM_STATE,\n max_iter=100\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n # Target 8 combinations (2*2*2)\n return ParameterGrid({\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 10],\n 'model__max_iter': [100, 200]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Competition evaluation metric is Root Mean Squared Logarithmic Error.\n # neg_mean_squared_log_error is the corresponding negative scikit-learn metric.\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n # Post-processing: Rings cannot be negative. Clipping ensures valid input for competition and metrics.\n # Lowest possible Rings in historical abalone data is 1.\n predictions = np.clip(predictions, 1, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14929798651227108} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n return base_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found. Cannot apply scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16465192742682772} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformations.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n return base_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\ndef run_pipeline():\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n full_pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model)\n ])\n full_pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(full_pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass\n\nif __name__ == \"__main__\":\n run_pipeline()", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features. Please check data loading.\")\n\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n base_model = Ridge(random_state=config.RANDOM_STATE)\n return base_model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply processing.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n# def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply robust scaling.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'. Cannot apply numerical transformation.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, make_scorer\nimport sklearn.model_selection\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nfrom lightgbm import LGBMRegressor\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n if not isinstance(config, types.SimpleNamespace):\n raise TypeError(\"config must be a types.SimpleNamespace\")\n\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Handle NaNs in target\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN]).reset_index(drop=True)\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Store test IDs before dropping\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Define columns to drop\n cols_to_drop_base = getattr(config, 'COLS_TO_DROP', [])\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + cols_to_drop_base if c in train_df.columns]\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + cols_to_drop_base if c in test_df.columns]\n\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n # Align columns\n missing_cols = set(data.x_train.columns) - set(data.x_test.columns)\n for col in missing_cols:\n data.x_test[col] = 0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"Feature columns in training and testing data do not match after alignment.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [31, 63],\n 'model__max_depth': [-1, 10]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Competition uses RMSLE. neg_mean_squared_log_error is appropriate for CV maximization.\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(\n n_splits=config.N_SPLITS,\n shuffle=True,\n random_state=config.RANDOM_STATE\n )\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not hasattr(data, 'x_test'):\n raise AttributeError(\"data object must have x_test attribute\")\n\n predictions = model.predict(data.x_test)\n\n # Rings must be positive. Log metric fails on negative values.\n # Standard practice is to clip at a minimum reasonable value.\n predictions = np.clip(predictions, 1.0, None)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14816854270584345} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.model_selection\nimport sklearn.linear_model\nfrom sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.linear_model import Ridge\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n\n y_pred = np.maximum(y_pred, 0)\n return np.sqrt(mean_squared_log_error(y_true, y_pred))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n if data.y_train.isnull().any():\n initial_rows = train_df.shape[0]\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n data.y_train = train_df[config.TARGET_COLUMN]\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows from training data due to NaN in target.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n if data.test_ids.isnull().any():\n raise ValueError(f\"Test ID column '{config.ID_COLUMN}' contains NaN values.\")\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in train_cols_set - test_cols_set:\n data.x_test[col] = 0 \n\n for col in test_cols_set - train_cols_set:\n data.x_test = data.x_test.drop(columns=[col])\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n if not categorical_features:\n raise ValueError(\"No categorical features found. 'Sex' column is expected.\")\n if 'Sex' not in categorical_features:\n raise ValueError(f\"'Sex' column not found in categorical features: {categorical_features}. Please check data loading.\")\n numerical_features = [f for f in numerical_features if f != 'Sex']\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after excluding 'Sex'.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = Ridge(random_state=config.RANDOM_STATE)\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__alpha': [0.01, 0.1, 1.0, 10.0] \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Scorer:\n return make_scorer(rmsle_scorer, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, 1, 29)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.163408269331438} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.PHYSICAL_COLS = [\n \"Length\", \"Diameter\", \"Height\", \"Whole_weight\",\n \"Shucked_weight\", \"Viscera_weight\", \"Shell_weight\"\n ]\n\n return config\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n data.x_test = data.x_test[train_cols] \n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')), \n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')) \n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough' \n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10], \n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions = np.clip(predictions, a_min=1.0, a_max=29.0)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14759149323051432} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.preprocessing\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n]\n\nScorerString = str \n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n data = types.SimpleNamespace()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN]\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n combined_df = pd.concat([data.x_train, data.x_test], ignore_index=True)\n\n data.x_train = combined_df.iloc[:len(train_df)].copy()\n data.x_test = combined_df.iloc[len(train_df):].copy()\n\n train_cols = data.x_train.columns.tolist()\n test_cols = data.x_test.columns.tolist()\n\n missing_in_test = set(train_cols) - set(test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols]\n\n if 'Sex' in data.x_train.columns:\n data.x_train['Sex'] = data.x_train['Sex'].astype('category')\n data.x_test['Sex'] = data.x_test['Sex'].astype('category')\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(random_state=config.RANDOM_STATE,\n objective='regression',\n n_jobs=-1,\n reg_alpha=0.1,\n reg_lambda=0.1,\n colsample_bytree=0.8,\n subsample=0.8,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [500, 1000],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31, 50],\n 'model__max_depth': [-1, 7, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n model = get_model(config)\n model.fit(data.x_train, data.y_train)\n submission = get_submission(model, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.14771915867885926} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n if 'Sex' in data.x_train.columns:\n data.x_train = data.x_train.drop(columns=['Sex'])\n if 'Sex' in data.x_test.columns:\n data.x_test = data.x_test.drop(columns=['Sex'])\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Protocol, Union, List, Iterator, Tuple, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Protocol, Union, List, Iterator, Tuple, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n data.x_train = train_df.drop(columns=[config.TARGET_COLUMN, config.ID_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n if 'Sex' not in train_df.columns or 'Sex' not in test_df.columns:\n raise ValueError(\"'Sex' column not found for feature engineering.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_train = pd.get_dummies(data.x_train, columns=['Sex'], prefix='Sex', dummy_na=False)\n data.x_test = pd.get_dummies(data.x_test, columns=['Sex'], prefix='Sex', dummy_na=False)\n\n sex_dummies_expected = ['Sex_M', 'Sex_F', 'Sex_I']\n for df in [data.x_train, data.x_test]:\n for sex_col in sex_dummies_expected:\n if sex_col not in df.columns:\n df[sex_col] = 0\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.14775019318384033} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14969006178627817} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Protocol, Union, List, Iterator, Tuple, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n TRANSFORM_TARGET_LOG1P=True,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n if config.TRANSFORM_TARGET_LOG1P:\n data.y_train = np.log1p(data.y_train_original)\n else:\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n if config.TRANSFORM_TARGET_LOG1P:\n return 'neg_mean_squared_error'\n else:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions_log_transformed = model.predict(data.x_test)\n if config.TRANSFORM_TARGET_LOG1P:\n predictions = np.expm1(predictions_log_transformed)\n else:\n predictions = predictions_log_transformed\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14746230603635158} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Protocol, Union, List, Iterator, Tuple, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train_original = combined_train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.preprocessing\nimport sklearn.impute\nimport sklearn.compose\nimport sklearn.pipeline\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport lightgbm as lgb\nfrom sklearn.metrics import make_scorer, mean_squared_log_error\nfrom typing import Any, Protocol, Union, List, Iterator, Tuple, runtime_checkable\nimport os\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n if not isinstance(y_true, np.ndarray):\n y_true = np.asarray(y_true)\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.asarray(y_pred)\n y_pred = np.maximum(y_pred, 0)\n mask = ~np.isnan(y_true) & ~np.isnan(y_pred)\n y_true_clean = y_true[mask]\n y_pred_clean = y_pred[mask]\n if y_true_clean.size == 0:\n return np.nan\n return np.sqrt(mean_squared_log_error(y_true_clean, y_pred_clean))\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n COLS_TO_DROP=[],\n HEIGHT_ZERO_REPLACE_NAN=True,\n PREDICTION_MIN=1,\n PREDICTION_MAX=29,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n data = types.SimpleNamespace()\n combined_train_df = train_df.drop(columns=[config.ID_COLUMN])\n if config.HEIGHT_ZERO_REPLACE_NAN:\n for df in [combined_train_df, test_df]:\n df['Height'] = df['Height'].replace(0, np.nan)\n data.y_train = combined_train_df[config.TARGET_COLUMN].copy()\n data.x_train = combined_train_df.drop(columns=[config.TARGET_COLUMN])\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.x_test = test_df.drop(columns=[config.ID_COLUMN])\n train_cols = set(data.x_train.columns)\n test_cols = set(data.x_test.columns)\n missing_in_test = list(train_cols - test_cols)\n for col in missing_in_test:\n data.x_test[col] = np.nan\n missing_in_train = list(test_cols - train_cols)\n for col in missing_in_train:\n data.x_train[col] = np.nan\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n categorical_features = []\n if 'Sex' in x_train.columns:\n if pd.api.types.is_object_dtype(x_train['Sex']) or pd.api.types.is_categorical_dtype(x_train['Sex']):\n categorical_features.append('Sex')\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_features = [f for f in numerical_features if f not in categorical_features]\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n objective='regression_l2',\n verbose=-1\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [200, 400],\n 'model__learning_rate': [0.01, 0.05],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [-1, 7],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False, needs_proba=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> KFold:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.16046402434580653} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n data.test_ids = test_df[config.ID_COLUMN].copy()\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n data.x_test = data.x_test[data.x_train.columns]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[], \n PREDICTION_MIN=1.0,\n PREDICTION_MAX=29.0\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ]\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.15115861992247548} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nimport sklearn.ensemble\nimport sklearn.model_selection\nfrom sklearn.model_selection import KFold, ParameterGrid\nfrom sklearn.preprocessing import RobustScaler, OneHotEncoder\nfrom sklearn.metrics import mean_squared_log_error, make_scorer\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\nCVSplitter = Union[\n KFold,\n sklearn.model_selection.RepeatedKFold,\n sklearn.model_selection.StratifiedKFold,\n sklearn.model_selection.RepeatedStratifiedKFold,\n sklearn.model_selection.StratifiedGroupKFold,\n sklearn.model_selection.GroupKFold,\n sklearn.model_selection.LeaveOneGroupOut,\n sklearn.model_selection.LeavePGroupsOut,\n sklearn.model_selection.GroupShuffleSplit,\n sklearn.model_selection.ShuffleSplit,\n sklearn.model_selection.StratifiedShuffleSplit,\n sklearn.model_selection.LeaveOneOut,\n sklearn.model_selection.LeavePOut,\n sklearn.model_selection.TimeSeriesSplit,\n sklearn.model_selection.PredefinedSplit\n]\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_df_path = config.INPUT_DIR / config.TRAIN_FILE\n test_df_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_df_path.exists():\n raise FileNotFoundError(f\"Training data not found at {train_df_path}\")\n if not test_df_path.exists():\n raise FileNotFoundError(f\"Test data not found at {test_df_path}\")\n\n train_df = pd.read_csv(train_df_path)\n test_df = pd.read_csv(test_df_path)\n\n data = types.SimpleNamespace()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN]\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + getattr(config, 'COLS_TO_DROP', [])\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = 0 \n\n extra_in_test = set(test_cols) - set(train_cols)\n if extra_in_test:\n data.x_test = data.x_test.drop(columns=list(extra_in_test))\n\n data.x_test = data.x_test[train_cols] \n\n if not data.x_train.columns.equals(data.x_test.columns):\n raise ValueError(\"x_train and x_test columns do not match after processing.\")\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return sklearn.ensemble.RandomForestRegressor(random_state=config.RANDOM_STATE)\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200],\n 'model__max_depth': [10, 20],\n 'model__min_samples_leaf': [5, 10],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=5, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, sklearn.pipeline.Pipeline):\n raise TypeError(\"Expected a scikit-learn Pipeline object for prediction.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n if not pd.api.types.is_numeric_dtype(submission_df[config.TARGET_COLUMN]):\n raise TypeError(f\"Submission target column '{config.TARGET_COLUMN}' is not numeric after post-processing.\")\n\n return submission_df", "y": 0.14944594363281627} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n objective='regression',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n config = get_config()\n try:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model_obj = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('model', model_obj)\n ])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\n except Exception:\n pass", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n predictions = np.clip(predictions, config.MIN_RINGS_PREDICTION, config.MAX_RINGS_PREDICTION)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nconfig = get_config()\ntry:\n data = load_data(config)\n preprocessor = get_preprocessor(config, data)\n model = get_model(config)\n pipeline = sklearn.pipeline.Pipeline(steps=[(\"preprocessor\", preprocessor), (\"model\", model)])\n pipeline.fit(data.x_train, data.y_train)\n submission = get_submission(pipeline, data, config)\n submission.to_csv(\"submission.csv\", index=False)\nexcept Exception:\n pass", "y": 0.14943173465602355} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS_PREDICTION=1,\n MAX_RINGS_PREDICTION=29,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_train_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if initial_train_rows > train_df.shape[0]:\n print(f\"Warning: Dropped {initial_train_rows - train_df.shape[0]} rows from train_df due to NaN in target.\")\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, runtime_checkable, Protocol\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit,\n ParameterGrid\n)\nimport lightgbm as lgb\n\n# Set the environment variable to suppress OpenBLAS verbose output\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\n# --- Protocols for type safety ---\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\nCVSplitter = Union[\n KFold, RepeatedKFold, StratifiedKFold, RepeatedStratifiedKFold,\n StratifiedGroupKFold, GroupKFold, LeaveOneGroupOut, LeavePGroupsOut,\n GroupShuffleSplit, ShuffleSplit, StratifiedShuffleSplit, LeaveOneOut,\n LeavePOut, TimeSeriesSplit, PredefinedSplit\n]\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n MIN_RINGS=1.0,\n MAX_RINGS=29.0,\n COLS_TO_DROP=[]\n )\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n train_path = config.INPUT_DIR / config.TRAIN_FILE\n test_path = config.INPUT_DIR / config.TEST_FILE\n\n if not train_path.exists():\n raise FileNotFoundError(f\"Train file not found at {train_path}\")\n if not test_path.exists():\n raise FileNotFoundError(f\"Test file not found at {test_path}\")\n\n train_df = pd.read_csv(train_path)\n test_df = pd.read_csv(test_path)\n\n # Handle missing targets\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df = train_df.dropna(subset=[config.TARGET_COLUMN])\n\n data = types.SimpleNamespace()\n data.y_train = train_df[config.TARGET_COLUMN].values\n\n # Preservation of test IDs\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n # Column dropping logic\n cols_to_drop_base = getattr(config, 'COLS_TO_DROP', [])\n\n train_drop = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN] + cols_to_drop_base if c in train_df.columns]\n data.x_train = train_df.drop(columns=train_drop)\n\n test_drop = [c for c in [config.ID_COLUMN] + cols_to_drop_base if c in test_df.columns]\n data.x_test = test_df.drop(columns=test_drop)\n\n # Feature Engineering: Basic interaction features for Abalone\n for df in [data.x_train, data.x_test]:\n df['Volume'] = df['Length'] * df['Diameter'] * df['Height']\n df['Weight_Ratio'] = df['Shell weight'] / (df['Whole weight'] + 1e-9)\n\n # Alignment\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=[np.number]).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.RobustScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n n_estimators=1000,\n learning_rate=0.05,\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n importance_type='gain',\n verbosity=-1\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__learning_rate': [0.03, 0.07],\n 'model__num_leaves': [31, 63],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0]\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n # Competition uses RMSLE, but for optimization, neg_root_mean_squared_error is a standard proxy.\n return 'neg_root_mean_squared_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n # Ensure prediction is numeric\n predictions = model.predict(data.x_test)\n\n # Post-processing: Target is count of rings, usually positive.\n # Clip to documented range of Abalone rings.\n predictions = np.clip(predictions, config.MIN_RINGS, config.MAX_RINGS)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481484382592307} +{"x": "s4e4\nimport types\nimport pathlib\nimport pandas as pd\nimport numpy as np\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.model_selection import KFold, ParameterGrid\nimport lightgbm as lgb\nfrom typing import Any, Callable, Protocol, Union, List, Iterator, Tuple, Set, Dict, runtime_checkable\n\nCVSplitter = Union[\n KFold\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n return types.SimpleNamespace(\n INPUT_DIR=pathlib.Path(\"/kaggle/input/playground-series-s4e4\"),\n TRAIN_FILE=\"train.csv\",\n TEST_FILE=\"test.csv\",\n ID_COLUMN=\"id\",\n TARGET_COLUMN=\"Rings\",\n RANDOM_STATE=42,\n N_SPLITS=5,\n )\n\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise ValueError(f\"Required data file not found: {e.filename}\") from e\n\n data = types.SimpleNamespace()\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train_original = train_df[config.TARGET_COLUMN].copy()\n data.y_train = data.y_train_original.values\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN]\n cols_to_drop_train_existing = [c for c in cols_to_drop_train if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train_existing)\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_test = [config.ID_COLUMN]\n cols_to_drop_test_existing = [c for c in cols_to_drop_test if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test_existing)\n\n train_cols_final = data.x_train.columns.tolist()\n\n for col in train_cols_final:\n if col not in data.x_test.columns:\n data.x_test[col] = np.nan\n\n data.x_test = data.x_test[train_cols_final]\n\n return data\n\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return lgb.LGBMRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n verbose=-1\n )\n\n\ndef get_param_grid(config: types.SimpleNamespace) -> ParameterGrid:\n return ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__num_leaves': [20, 31],\n 'model__max_depth': [5, 7],\n 'model__reg_alpha': [0.1, 0.5],\n 'model__reg_lambda': [0.1, 0.5],\n })\n\n\ndef get_scorer_func(config: types.SimpleNamespace) -> ScorerString:\n return 'neg_root_mean_squared_error'\n\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> CVSplitter:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\n\ndef get_submission(model: sklearn.pipeline.Pipeline, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df", "y": 0.1481807575108202} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median')),\n ('scaler', sklearn.preprocessing.StandardScaler())\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14778357751251783} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0 \n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', 'passthrough', numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist',\n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256],\n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n\n config.RANDOM_STATE = 42\n\n config.N_SPLITS = 5\n\n config.COLS_TO_DROP = []\n\n config.PREDICTION_MIN = 1.0\n config.PREDICTION_MAX = 29.0 \n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n\n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n\n data.x_test = data.x_test[train_cols]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.PREDICTION_MIN, config.PREDICTION_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"FileNotFoundError: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Feature mismatch: {missing_in_train}\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Type error: {type(model)}\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n configuration = get_config()\n try:\n dataset = load_data(configuration)\n transformer = get_preprocessor(configuration, dataset)\n regressor = get_model(configuration)\n pipe = sklearn.pipeline.Pipeline(steps=[(\"pre\", transformer), (\"model\", regressor)])\n pipe.fit(dataset.x_train, dataset.y_train)\n final_submission = get_submission(pipe, dataset, configuration)\n print(final_submission.head())\n except Exception as error:\n print(error)", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Protocol, Union, List, Iterator, Tuple, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error, make_scorer\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SUBMISSION_FILE = \"sample_submission.csv\"\n\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.COLS_TO_DROP = []\n\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n\n config.TARGET_MIN = 1.0\n config.TARGET_MAX = 29.0\n\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n\n if config.ID_COLUMN not in train_df.columns or config.ID_COLUMN not in test_df.columns:\n raise ValueError(f\"ID column '{config.ID_COLUMN}' not found in train or test data.\")\n if config.TARGET_COLUMN not in train_df.columns:\n raise ValueError(f\"Target column '{config.TARGET_COLUMN}' not found in train data.\")\n\n data.test_ids = test_df[config.ID_COLUMN].copy()\n\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n\n cols_to_drop_train = [c for c in [config.ID_COLUMN, config.TARGET_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in train_df.columns]\n data.x_train = train_df.drop(columns=cols_to_drop_train)\n\n cols_to_drop_test = [c for c in [config.ID_COLUMN, 'Sex'] + config.COLS_TO_DROP if c in test_df.columns]\n data.x_test = test_df.drop(columns=cols_to_drop_test)\n\n train_cols_set = set(data.x_train.columns)\n test_cols_set = set(data.x_test.columns)\n\n for col in (train_cols_set - test_cols_set):\n data.x_test[col] = 0.0\n\n for col in (test_cols_set - train_cols_set):\n data.x_train[col] = 0.0\n\n data.x_test = data.x_test[data.x_train.columns]\n\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n numerical_features = data.x_train.columns.tolist()\n\n if not numerical_features:\n raise ValueError(\"No numerical features found after load_data, preprocessor cannot be created.\")\n\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n return xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n )\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [100, 200, 300],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.7, 0.9],\n 'model__colsample_bytree': [0.7, 0.9],\n 'model__reg_alpha': [0.1, 1.0],\n 'model__reg_lambda': [0.1, 1.0],\n })\n\ndef rmsle_scorer_func(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n y_pred_clipped = np.clip(y_pred, 1.0, None)\n return np.sqrt(mean_squared_log_error(y_true, y_pred_clipped))\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return make_scorer(rmsle_scorer_func, greater_is_better=False)\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter | CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Any, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n predictions = model.predict(data.x_test)\n\n predictions_clipped = np.clip(predictions, config.TARGET_MIN, config.TARGET_MAX)\n\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions_clipped\n })\n\n return submission_df", "y": 0.14932339319726443} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n configuration = get_config()\n try:\n dataset = load_data(configuration)\n transformer = get_preprocessor(configuration, dataset)\n regressor = get_model(configuration)\n pipe = sklearn.pipeline.Pipeline(steps=[(\"pre\", transformer), (\"model\", regressor)])\n pipe.fit(dataset.x_train, dataset.y_train)\n final_submission = get_submission(pipe, dataset, configuration)\n print(final_submission.head())\n except Exception as error:\n print(error)", "y": 0.14774718864638517} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features)\n ],\n remainder='drop'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n objective='reg:squaredlogerror',\n eval_metric='rmsle', \n random_state=config.RANDOM_STATE,\n n_jobs=-1, \n tree_method='hist', \n verbose=0\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n cfg = get_config()\n try:\n dt = load_data(cfg)\n prep = get_preprocessor(cfg, dt)\n raw_mdl = get_model(cfg)\n pipe = sklearn.pipeline.Pipeline(steps=[('preprocessor', prep), ('model', raw_mdl)])\n pipe.fit(dt.x_train, dt.y_train)\n sub = get_submission(pipe, dt, cfg)\n print(sub.head())\n except Exception:\n pass", "y": 0.1492298511262347} +{"x": "s4e4\nimport json\nimport os\nimport types\nimport pathlib\nimport signal\nimport sys\nimport time\nimport tempfile\nfrom typing import Any, Callable, Dict, Set, Union, List, Iterator, Tuple, Protocol, runtime_checkable\n\nimport numpy as np\nimport pandas as pd\nimport sklearn.pipeline\nimport sklearn.base\nimport sklearn.preprocessing\nimport sklearn.compose\nimport sklearn.impute\nfrom sklearn.metrics import get_scorer, get_scorer_names, mean_squared_log_error\nimport sklearn.metrics\nimport sklearn.dummy\nimport sklearn.model_selection\nimport sklearn.ensemble\nimport sklearn.linear_model\nimport sklearn.multioutput\nimport xgboost as xgb\n\nos.environ['OPENBLAS_VERBOSE'] = '0'\n\nfrom sklearn.model_selection import (\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n)\n\nCVSplitter = Union[\n KFold,\n RepeatedKFold,\n StratifiedKFold,\n RepeatedStratifiedKFold,\n StratifiedGroupKFold,\n GroupKFold,\n LeaveOneGroupOut,\n LeavePGroupsOut,\n GroupShuffleSplit,\n ShuffleSplit,\n StratifiedShuffleSplit,\n LeaveOneOut,\n LeavePOut,\n TimeSeriesSplit,\n PredefinedSplit\n]\n\n@runtime_checkable\nclass Model(Protocol):\n def fit(self, x: Any, y: Any, *args: Any, **kwargs: Any) -> None: ...\n def predict(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n def set_params(self, **kwargs: Any) -> None: ...\n\n@runtime_checkable\nclass Processor(Protocol):\n def fit(self, x: Any, *args: Any, **kwargs: Any) -> None: ...\n def transform(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass ClassifierModel(Model, Protocol):\n def predict_proba(self, x: Any, *args: Any, **kwargs: Any) -> Any: ...\n\n@runtime_checkable\nclass CustomCVSplitter(Protocol):\n def get_n_splits(self, X: Any = None, y: Any = None, groups: Any = None) -> int: ...\n def split(self, X: Any, y: Any = None, groups: Any = None) -> Iterator[Tuple[np.ndarray, np.ndarray]]: ...\n\nScorerString = str\n\n@runtime_checkable\nclass Scorer(Protocol):\n def __call__(self, model: Model, x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> float:\n ...\n\n\ndef get_config() -> types.SimpleNamespace:\n config = types.SimpleNamespace()\n config.INPUT_DIR = pathlib.Path(\"/kaggle/input/playground-series-s4e4\")\n config.TRAIN_FILE = \"train.csv\"\n config.TEST_FILE = \"test.csv\"\n config.SAMPLE_SUBMISSION_FILE = \"sample_submission.csv\"\n config.ID_COLUMN = \"id\"\n config.TARGET_COLUMN = \"Rings\"\n config.RANDOM_STATE = 42\n config.N_SPLITS = 5\n config.COLS_TO_DROP = []\n return config\n\ndef load_data(config: types.SimpleNamespace) -> types.SimpleNamespace:\n data = types.SimpleNamespace()\n try:\n train_df = pd.read_csv(config.INPUT_DIR / config.TRAIN_FILE)\n test_df = pd.read_csv(config.INPUT_DIR / config.TEST_FILE)\n except FileNotFoundError as e:\n raise FileNotFoundError(f\"Ensure '{config.INPUT_DIR}' and '{config.TRAIN_FILE}/{config.TEST_FILE}' exist. Error: {e}\")\n if train_df[config.TARGET_COLUMN].isnull().any():\n initial_rows = train_df.shape[0]\n train_df.dropna(subset=[config.TARGET_COLUMN], inplace=True)\n if train_df.shape[0] < initial_rows:\n print(f\"Dropped {initial_rows - train_df.shape[0]} rows with NaN in target column.\")\n data.y_train = train_df[config.TARGET_COLUMN].copy()\n data.test_ids = test_df[config.ID_COLUMN].copy()\n cols_to_drop_train = [config.ID_COLUMN, config.TARGET_COLUMN] + config.COLS_TO_DROP\n data.x_train = train_df.drop(columns=[c for c in cols_to_drop_train if c in train_df.columns])\n cols_to_drop_test = [config.ID_COLUMN] + config.COLS_TO_DROP\n data.x_test = test_df.drop(columns=[c for c in cols_to_drop_test if c in test_df.columns])\n train_cols = data.x_train.columns\n test_cols = data.x_test.columns\n missing_in_test = set(train_cols) - set(test_cols)\n for c in missing_in_test:\n data.x_test[c] = np.nan \n missing_in_train = set(test_cols) - set(train_cols)\n if missing_in_train:\n raise ValueError(f\"Features {missing_in_train} present in test set but not in train set after FE. Column mismatch.\")\n data.x_test = data.x_test[train_cols]\n return data\n\ndef get_preprocessor(config: types.SimpleNamespace, data: types.SimpleNamespace) -> Processor:\n x_train = data.x_train\n numerical_features = x_train.select_dtypes(include=np.number).columns.tolist()\n categorical_features = x_train.select_dtypes(include=['object', 'category']).columns.tolist()\n numerical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='median'))\n ])\n categorical_transformer = sklearn.pipeline.Pipeline(steps=[\n ('imputer', sklearn.impute.SimpleImputer(strategy='most_frequent')),\n ('onehot', sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore'))\n ])\n preprocessor = sklearn.compose.ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numerical_features),\n ('cat', categorical_transformer, categorical_features)\n ],\n remainder='passthrough'\n )\n return preprocessor\n\ndef get_model(config: types.SimpleNamespace) -> Model:\n model = xgb.XGBRegressor(\n random_state=config.RANDOM_STATE,\n n_jobs=-1,\n tree_method='hist'\n )\n return model\n\ndef get_param_grid(config: types.SimpleNamespace) -> sklearn.model_selection.ParameterGrid:\n return sklearn.model_selection.ParameterGrid({\n 'model__n_estimators': [150, 250],\n 'model__learning_rate': [0.05, 0.1],\n 'model__max_depth': [5, 7],\n 'model__subsample': [0.8, 1.0],\n 'model__colsample_bytree': [0.8, 1.0],\n 'model__tree_method': ['exact', 'hist'],\n 'model__max_bin': [128, 256], \n })\n\ndef get_scorer_func(config: types.SimpleNamespace) -> Union[Scorer, ScorerString]:\n return 'neg_mean_squared_log_error'\n\ndef get_cv_splitter(config: types.SimpleNamespace) -> Union[CVSplitter, CustomCVSplitter]:\n return KFold(n_splits=config.N_SPLITS, shuffle=True, random_state=config.RANDOM_STATE)\n\ndef get_submission(model: Model, data: types.SimpleNamespace, config: types.SimpleNamespace) -> pd.DataFrame:\n if not isinstance(model, Model):\n raise TypeError(f\"Expected 'model' to be an instance of Model protocol, got {type(model)}\")\n if not isinstance(data, types.SimpleNamespace):\n raise TypeError(f\"Expected 'data' to be an instance of types.SimpleNamespace, got {type(data)}\")\n if not hasattr(data, 'x_test') or not hasattr(data, 'test_ids'):\n raise AttributeError(\"Data namespace must contain 'x_test' and 'test_ids' for submission generation.\")\n if data.x_test.shape[0] != len(data.test_ids):\n raise ValueError(\"Mismatch between number of test samples and test IDs. Data integrity error.\")\n predictions = model.predict(data.x_test)\n submission_df = pd.DataFrame({\n config.ID_COLUMN: data.test_ids,\n config.TARGET_COLUMN: predictions\n })\n return submission_df\n\nif __name__ == \"__main__\":\n configuration = get_config()\n try:\n dataset = load_data(configuration)\n transformer = get_preprocessor(configuration, dataset)\n regressor = get_model(configuration)\n pipe = sklearn.pipeline.Pipeline(steps=[(\"pre\", transformer), (\"model\", regressor)])\n pipe.fit(dataset.x_train, dataset.y_train)\n final_submission = get_submission(pipe, dataset, configuration)\n print(final_submission.head())\n except Exception as error:\n print(error)", "y": 0.14750854608724917}