kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
1,058,030 | def update_features(df,
answered_correctly_u_sum,
answered_correctly_q_sum,
timestamp_u_incorrect,
explanation_qtrue_sum,
task_container_sum,
latest_u_theta,
latest_q_beta,
answered_correctly_u_count,
answered_correctly_q_count,
tags_sum,
answered_correctly_u_sum_field,
answered_correctly_difficulty_weighted_sum,
answe... | ids = test_df1['PassengerId']
model = SVC(random_state=0, gamma="auto")
model.fit(X_new,Y)
test_df1_pred = test_df1[X_new.columns]
predictions = model.predict(test_df1_pred)
output = pd.DataFrame({'PassengerId' : ids,
'Survived' : predictions})
output.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
2,304,442 | def read_and_preprocess(feature_engineering = False, n_split = 3):
train_pickle = '.. /input/riiid-cross-validation-files/cv1_train.pickle'
valid_pickle = '.. /input/riiid-cross-validation-files/cv1_valid.pickle'
question_metadata_file = '.. /input/question-metadate-new-new/question_metadata_new_new.csv'
question_data_... | def feature_engg_train(df):
df['Title'] = df['Name'].map(lambda name:name.split('.')[0].split(',')[1].strip())
titles_dict_train = {}
for title in ['Capt','Col','Major']:
titles_dict_train[title] = 'Officer'
for title in ['Rev','Dr']:
titles_dict_train[title] = 'Other'
for title in ['Don','the Countess','Jonkheer','La... | Titanic - Machine Learning from Disaster |
2,304,442 | questions_df, prior_question_elapsed_time_mean, features_dicts = read_and_preprocess(feature_engineering = False, n_split = 25)
LGBM_model = lgb.Booster(model_file = '.. /input/lgbm-v921-01/lgbm_model_V921_0.1.lgb' )<define_variables> | %matplotlib inline
sns.set_style('white')
| Titanic - Machine Learning from Disaster |
2,304,442 | answered_correctly_u_count = features_dicts['answered_correctly_u_count']
answered_correctly_u_sum = features_dicts['answered_correctly_u_sum']
elapsed_time_u_sum = features_dicts['elapsed_time_u_sum']
explanation_u_sum = features_dicts['explanation_u_sum']
answered_correctly_q_count = features_dicts['answered_correctl... | df_train = pd.read_csv('.. /input/train.csv')
df_test = pd.read_csv('.. /input/test.csv' ) | Titanic - Machine Learning from Disaster |
2,304,442 | MAX_SEQ = 240
ACCEPTED_USER_CONTENT_SIZE = 2
EMBED_SIZE = 256
BATCH_SIZE = 64+32
DROPOUT = 0.1
n_skill = 13523
print(n_skill)
group = joblib.load(".. /input/new-sakt-dataset/group.pkl.zip")
class FFN(nn.Module):
def __init__(self, state_size = 200, forward_expansion = 1, bn_size = MAX_SEQ - 1, dropout=0.2):
super(FFN... | df_train = feature_engg_train(df_train)
df_test = feature_engg_test(df_test)
features_train = df_train.drop(['Survived','Cabin_T'],axis=1)
target_train = df_train['Survived']
features_test = df_test | Titanic - Machine Learning from Disaster |
2,304,442 | env = riiideducation.make_env()
iter_test = env.iter_test()
set_predict = env.predict<feature_engineering> | clf=RandomForestClassifier(n_estimators=100,random_state=42)
clf=clf.fit(features_train,target_train ) | Titanic - Machine Learning from Disaster |
2,304,442 | %%time
w = 0.225
previous_test_df = None
for(test_df, sample_prediction_df)in iter_test:
if previous_test_df is not None:
previous_test_df[TARGET] = eval(test_df["prior_group_answers_correct"].iloc[0])
update_features(previous_test_df,
answered_correctly_u_sum,
answered_correctly_q_sum,
timestamp_u_incorrect,
explanat... | model = SelectFromModel(clf, prefit=True,threshold='median')
train_reduced = model.transform(features_train)
train_reduced.shape | Titanic - Machine Learning from Disaster |
2,304,442 | DEFAULT_SEED = 42
PLOT_SHAP = False
MAX_QUESTIONS = 14000
VAL_SIZE = 2500000
USE_DATA_RATIO = 0.5
TS_SCALING = 1000*3600
LEARNING_RATE = 0.1
MAX_BIN = 364
NUM_LEAVES = 445
FEATURE_FRACTION = 0.639
BAGGING_FRACTION = 0.842
BAGGING_FREQ = 19
NUM_BOOST_ROUNDS = 10000
EARLY_STOP_ROUNDS = 20
VERBOSE_EVAL = 50
TRAIN_FILE_PAT... | X_train,X_test,y_train,y_test = train_test_split(train_reduced,target_train,random_state=42)
param_grid_rf = {'n_estimators':[100,500],'min_samples_split':[2,5],\
'max_depth':[5,10],'min_samples_leaf':[5,10]}
grid_rf = GridSearchCV(estimator=RandomForestClassifier(random_state=42, oob_score=True, warm_start=True),para... | Titanic - Machine Learning from Disaster |
2,304,442 | target_col = 'answered_correctly'
data_types_dict = {
'row_id': 'int64',
'timestamp': 'int64',
'user_id': 'int32',
'content_id': 'int16',
'content_type_id': 'int8',
'task_container_id': 'int16',
'answered_correctly': 'int8',
'prior_question_elapsed_time': 'float32',
'prior_question_had_explanation': 'int8'
}
with trace... | print("Best parameters : {}".format(grid_rf.best_params_))
print("Best cross-validation score : {:.2f}".format(grid_rf.best_score_)) | Titanic - Machine Learning from Disaster |
2,304,442 | <import_modules><EOS> | clf_rf = grid_rf.best_estimator_
clf_rf.fit(train_reduced,target_train)
target_test = clf_rf.predict(test_reduced)
df_test['Survived'] = target_test
df_test[['PassengerId','Survived']].to_csv('rf-kaggle-submit.csv',index=False ) | Titanic - Machine Learning from Disaster |
624,751 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<set_options> | %matplotlib inline | Titanic - Machine Learning from Disaster |
624,751 | def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
set_seed(42 )<init_hyperparams> | train_df = pd.read_csv('.. /input/train.csv')
test_df = pd.read_csv('.. /input/test.csv')
combine = [train_df, test_df] | Titanic - Machine Learning from Disaster |
624,751 | cfg = {
'format_version': 4,
'data_path': "/kaggle/input/lyft-motion-prediction-autonomous-vehicles",
'model_params': {
'model_architecture': 'resnet34',
'history_num_frames': 10,
'history_step_size': 1,
'history_delta_time': 0.55,
'future_num_frames': 50,
'future_step_size': 1,
'future_delta_time': 0.55,
'model_name':... | print("Percentage of survival in the train set: {}%".format(round(sum(train_df.Survived)/train_df.Survived.count() , 2)) ) | Titanic - Machine Learning from Disaster |
624,751 | DIR_INPUT = cfg["data_path"]
os.environ["L5KIT_DATA_FOLDER"] = DIR_INPUT
dm = LocalDataManager(None )<create_dataframe> | train_df[['Pclass', 'Survived']].groupby(['Pclass'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | train_cfg = cfg["train_data_loader"]
rasterizer = build_rasterizer(cfg, dm)
train_zarr = ChunkedDataset(dm.require(train_cfg["key"])).open()
train_dataset = AgentDataset(cfg, train_zarr, rasterizer)
train_dataloader = DataLoader(train_dataset, shuffle=train_cfg["shuffle"], batch_size=train_cfg["batch_size"],
num_work... | train_df[["Sex", "Survived"]].groupby(['Sex'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | test_cfg = cfg["test_data_loader"]
rasterizer = build_rasterizer(cfg, dm)
test_zarr = ChunkedDataset(dm.require(test_cfg["key"])).open()
test_mask = np.load(f"{DIR_INPUT}/scenes/mask.npz")["arr_0"]
test_dataset = AgentDataset(cfg, test_zarr, rasterizer, agents_mask=test_mask)
test_dataloader = DataLoader(test_dataset... | train_df[["SibSp", "Survived"]].groupby(['SibSp'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | class LyftMultiModel(nn.Module):
def __init__(self, cfg: Dict, num_modes=3):
super().__init__()
architecture = cfg["model_params"]["model_architecture"]
backbone = eval(architecture )(pretrained=True, progress=True)
self.backbone = backbone
num_history_channels =(cfg["model_params"]["history_num_frames"] + 1)* 2
num_i... | train_df[["Parch", "Survived"]].groupby(['Parch'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | def forward(data, model, device, criterion = pytorch_neg_multi_log_likelihood_batch):
inputs = data["image"].to(device)
target_availabilities = data["target_availabilities"].to(device)
targets = data["target_positions"].to(device)
preds, confidences = model(inputs)
loss = criterion(targets, preds, confidences, targ... | train_df[["Embarked", "Survived"]].groupby(['Embarked'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = LyftMultiModel(cfg)
print(f'device {device}')
weight_path = cfg["model_params"]["weight_path"]
if weight_path:
checkpoint = torch.load(weight_path)
model.load_state_dict(checkpoint['state_dict'])
model.cuda()
optimizer = optim.AdamW(mode... | for dataset in combine:
df = dataset.groupby(['Sex', 'Pclass'] ).size().unstack(0)
df['fem_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]]))
print(df)
print("_"*40 ) | Titanic - Machine Learning from Disaster |
624,751 | if cfg["model_params"]["train"]:
tr_it = iter(train_dataloader)
progress_bar = tqdm(range(cfg["train_params"]["max_num_steps"]))
num_iter = cfg["train_params"]["max_num_steps"]
losses_train = []
iterations = []
metrics = []
times = []
model_name = cfg["model_params"]["model_name"]
start = time.time()
for i in progress... | train_df[["Sex", "Pclass", "Survived"]].groupby(['Sex', 'Pclass'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | pred_path = 'submission.csv'
write_pred_csv(pred_path,
timestamps=np.concatenate(timestamps),
track_ids=np.concatenate(agent_ids),
coords=np.concatenate(future_coords_offsets_pd),
confs = np.concatenate(confidences_list)
)<import_modules> | for dataset in combine:
df = dataset.groupby(['Sex', 'Parch'] ).size().unstack(0 ).fillna(0)
df['male_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]]))
df['fem_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]]))
print(df)
print("_"*40 ) | Titanic - Machine Learning from Disaster |
624,751 | warnings.filterwarnings("ignore" )<import_modules> | train_df[["Sex", "Parch", "Survived"]].groupby(['Sex', 'Parch'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | l5kit.__version__<set_options> | for dataset in combine:
df = dataset.groupby(['Sex', 'SibSp'] ).size().unstack(0 ).fillna(0)
df['male_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]]))
df['fem_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]]))
print(df)
print("_"*40 ) | Titanic - Machine Learning from Disaster |
624,751 | def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
set_seed(42 )<init_hyperparams> | train_df[["Sex", "SibSp", "Survived"]].groupby(['Sex', 'SibSp'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | cfg = {
'format_version': 4,
'data_path': "/kaggle/input/lyft-motion-prediction-autonomous-vehicles",
'model_params': {
'model_architecture': 'resnet34',
'history_num_frames': 10,
'history_step_size': 1,
'history_delta_time': 0.1,
'future_num_frames': 50,
'future_step_size': 1,
'future_delta_time': 0.1,
'model_name': "... | for dataset in combine:
df = dataset.groupby(['Sex', 'Embarked'] ).size().unstack(0)
df['male_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]]))
df['fem_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]]))
print(df)
print("_"*40 ) | Titanic - Machine Learning from Disaster |
624,751 | DIR_INPUT = cfg["data_path"]
os.environ["L5KIT_DATA_FOLDER"] = DIR_INPUT
dm = LocalDataManager(None )<create_dataframe> | train_df[["Embarked", "Sex", "Survived"]].groupby(['Sex', 'Embarked'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | train_cfg = cfg["train_data_loader"]
rasterizer = build_rasterizer(cfg, dm)
train_zarr = ChunkedDataset(dm.require(train_cfg["key"])).open()
train_dataset = AgentDataset(cfg, train_zarr, rasterizer)
train_dataloader = DataLoader(train_dataset, shuffle=train_cfg["shuffle"], batch_size=train_cfg["batch_size"],
num_work... | for dataset in combine:
df = dataset.groupby(['Pclass', 'Embarked'] ).size().unstack(0)
df['first_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]] + df[df.columns[2]]))
df['second_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]] + df[df.columns[2]]))
df['third_perc'] =(df[df.columns[2... | Titanic - Machine Learning from Disaster |
624,751 | test_cfg = cfg["test_data_loader"]
rasterizer = build_rasterizer(cfg, dm)
test_zarr = ChunkedDataset(dm.require(test_cfg["key"])).open()
test_mask = np.load(f"{DIR_INPUT}/scenes/mask.npz")["arr_0"]
test_dataset = AgentDataset(cfg, test_zarr, rasterizer, agents_mask=test_mask)
test_dataloader = DataLoader(test_dataset... | train_df[["Embarked", "Pclass", "Survived"]].groupby(['Embarked', 'Pclass'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | class LyftMultiModel(nn.Module):
def __init__(self, cfg: Dict, num_modes=3):
super().__init__()
architecture = cfg["model_params"]["model_architecture"]
backbone = eval(architecture )(pretrained=True, progress=True)
self.backbone = backbone
num_history_channels =(cfg["model_params"]["history_num_frames"] + 1)* 2
num_i... | for dataset in combine:
fil1 =(dataset.Cabin.isnull())
fil2 =(dataset.Cabin.notnull())
dataset.loc[fil1, 'Cabin'] = 0
dataset.loc[fil2, 'Cabin'] = 1
dataset.Cabin = pd.to_numeric(dataset['Cabin'])
print(train_df.Cabin.value_counts())
print("_"*40)
print(test_df.Cabin.value_counts() ) | Titanic - Machine Learning from Disaster |
624,751 | def forward(data, model, device, criterion = pytorch_neg_multi_log_likelihood_batch):
inputs = data["image"].to(device)
target_availabilities = data["target_availabilities"].to(device)
targets = data["target_positions"].to(device)
preds, confidences = model(inputs)
loss = criterion(targets, preds, confidences, targ... | train_df[['Cabin', 'Survived']].groupby(['Cabin'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = LyftMultiModel(cfg)
weight_path = cfg["model_params"]["weight_path"]
if weight_path:
model.load_state_dict(torch.load(weight_path))
model.to(device)
optimizer = optim.Adam(model.parameters() , lr=cfg["model_params"]["lr"])
print(f'devic... | for dataset in combine:
df = dataset.groupby(['Sex', 'Cabin'] ).size().unstack(0)
df['fem_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]]))
df['male_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]]))
print(df)
print("_"*40 ) | Titanic - Machine Learning from Disaster |
624,751 | print(model )<init_hyperparams> | train_df[["Cabin", "Sex", "Survived"]].groupby(['Sex', 'Cabin'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | if cfg["model_params"]["train"]:
tr_it = iter(train_dataloader)
progress_bar = tqdm(range(cfg["train_params"]["max_num_steps"]))
num_iter = cfg["train_params"]["max_num_steps"]
losses_train = []
iterations = []
metrics = []
times = []
model_name = cfg["model_params"]["model_name"]
start = time.time()
for i in progress... | for dataset in combine:
df = dataset.groupby(['Cabin', 'Pclass'] ).size().unstack(0)
df['miss_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]]))
print(df)
print("_"*40 ) | Titanic - Machine Learning from Disaster |
624,751 | pred_path = 'submission.csv'
write_pred_csv(pred_path,
timestamps=np.concatenate(timestamps),
track_ids=np.concatenate(agent_ids),
coords=np.concatenate(future_coords_offsets_pd),
confs = np.concatenate(confidences_list)
)<import_modules> | train_df[["Cabin", "Pclass", "Survived"]].groupby(['Pclass', 'Cabin'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | TL_FACE_DTYPE,
filter_agents_by_labels,
filter_tl_faces_by_frames,
get_agents_slice_from_frames,
get_tl_faces_slice_from_frames,
)
def generate_kinetic_agent_sample(
state_index: int,
frames: np.ndarray,
agents: np.ndarray,
tl_faces: np.ndarray,
selected_track_id: Optional[int],
raster_size: Tuple[int, int],
pixel_s... | train_df[train_df.Embarked.isnull() ] | Titanic - Machine Learning from Disaster |
624,751 | MIN_FRAME_HISTORY = 10
MIN_FRAME_FUTURE = 1
class KineticDataset(AgentDataset):
def __init__(
self,
cfg: dict,
zarr_dataset: ChunkedDataset,
rasterizer: Rasterizer,
perturbation: Optional[Perturbation] = None,
agents_mask: Optional[np.ndarray] = None,
min_frame_history: int = MIN_FRAME_HISTORY,
min_frame_future: int =... | test_df[test_df.Fare.isnull() ] | Titanic - Machine Learning from Disaster |
624,751 | def trim_network_at_index(network: nn.Module, index: int = -1)-> nn.Module:
assert index < 0, f"Param index must be negative.Received {index}."
return nn.Sequential(*list(network.children())[:index])
class MnasBackbone(nn.Module):
def __init__(self, num_in_channels: int = 3):
super().__init__()
model = mnasnet1_... | fil =(( train_df.Pclass == 1)&(train_df.SibSp == 0)&(train_df.Parch == 0)
&(train_df.Sex == 'female'))
mis = train_df[fil].Embarked.mode()
print(mis)
fil = train_df.Embarked.isnull()
train_df.loc[fil, 'Embarked'] = 'C'
print("_"*40)
print(train_df.Embarked.value_counts(dropna = False)) | Titanic - Machine Learning from Disaster |
624,751 | ASV_DIM = 3
def calculate_backbone_feature_dim(backbone, input_shape: Tuple[int, int, int])-> int:
tensor = torch.ones(1, *input_shape)
output_feat = backbone.forward(tensor)
return output_feat.shape[-1]
class LyftNet(nn.Module):
def __init__(self, backbone: nn.Module, num_modes: int, num_targets: int, num_kineti... | fil =(( test_df.Pclass == 3)&(test_df.SibSp == 0)&(test_df.Parch == 0)
&(test_df.Cabin == 0)&(test_df.Sex == 'male')&(test_df.Embarked == 'S'))
mis = round(test_df[fil].Fare.median() , 4)
print(mis)
fil = test_df.Fare.isnull()
test_df.loc[fil, 'Fare'] = mis
print("_"*40)
print(test_df.Fare.isnull().value_counts(dro... | Titanic - Machine Learning from Disaster |
624,751 | class LyftManager:
def __init__(self, config, data_path, device, num_modes=3, verbose=False):
self.cfg = config
self.data_path = data_path
self.device = device
self.verbose = verbose
num_history_channels =(self.cfg["model_params"]["history_num_frames"] + 1)* 2
num_in_channels = 3 + num_history_channels
self.backbone = ... | for dataset in combine:
dataset['MisAge'] = 0
fil =(dataset.Age.isnull())
dataset.loc[fil, 'MisAge'] = 1
print(train_df.MisAge.value_counts())
print("_"*40)
print(test_df.MisAge.value_counts())
print("_"*40)
print("_"*40)
train_df[['MisAge', 'Survived']].groupby(['MisAge'],
as_index=True ).mean().sort_values(by='... | Titanic - Machine Learning from Disaster |
624,751 | data_path = "/kaggle/input/lyft-motion-prediction-autonomous-vehicles"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
config = load_config_data("/kaggle/input/lyftnet/config_lyftnet.yaml")
config["test_data_loader"]["batch_size"] = 16
config["test_data_loader"]["num_workers"] = 8
checkpoint_p... | for dataset in combine:
df = dataset.groupby(['MisAge', 'Sex'] ).size().unstack(0)
df['miss_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]]))
df['nomiss_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]]))
print(df)
print("_"*40 ) | Titanic - Machine Learning from Disaster |
624,751 |
<import_modules> | train_df[["Sex", "MisAge", "Survived"]].groupby(['Sex', 'MisAge'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | warnings.filterwarnings("ignore")
print(l5kit.__version__ )<set_options> | for dataset in combine:
df = dataset.groupby(['MisAge', 'Pclass'] ).size().unstack(0)
df['miss_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]]))
df['nomiss_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]]))
print(df)
print("_"*40 ) | Titanic - Machine Learning from Disaster |
624,751 | def memory(verbose=True):
mem = psutil.virtual_memory()
gb = 1024*1024*1024
if verbose:
print('Physical memory:',
'%.2f GB(used),'%(( mem.total - mem.available)/ gb),
'%.2f GB(available)'%(( mem.available)/ gb), '/',
'%.2f GB'%(mem.total / gb))
return(mem.total - mem.available)/ gb
def gc_memory(verbose=True):
m = gc.c... | train_df[["Pclass", "MisAge", "Survived"]].groupby(['Pclass', 'MisAge'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
set_seed(42 )<init_hyperparams> | train_df[["Cabin", "MisAge", "Survived"]].groupby(['Cabin', 'MisAge'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | cfg = {
'format_version': 4,
'data_path': '/kaggle/input/lyft-motion-prediction-autonomous-vehicles',
'model_params': {
'first_layer_bias': False,
'pretrained': True,
'multi_mode': True,
'model_architecture': 'resnet101',
'history_num_frames': 10,
'history_step_size': 1,
'history_delta_time': 0.1,
'future_num_frames': ... | fil =(train_df.Age.isnull())
print("By class:")
print(train_df[fil].Pclass.value_counts())
print("_"*40)
print(train_df[train_df.MisAge == 0].Pclass.value_counts())
print("_"*40)
print("_"*40)
print("By sex:")
print(train_df[fil].Sex.value_counts())
print("_"*40)
print(train_df[train_df.MisAge == 0].Sex.value... | Titanic - Machine Learning from Disaster |
624,751 | if cfg['combine']:
execute('submission_1.csv')
cfg["model_params"]["weight_path"] = '.. /input/resnet34-bestscore-epoch-weights/model_state_last.pth'
cfg['model_params']['model_architecture'] = 'resnet34'
execute('submission_2.csv')
else:
execute('submission.csv')
<define_variables> | fil =(test_df.Age.isnull())
print("By class:")
print(test_df[fil].Pclass.value_counts())
print("_"*40)
print(test_df[test_df.MisAge == 0].Pclass.value_counts())
print("_"*40)
print("_"*40)
print("By sex:")
print(test_df[fil].Sex.value_counts())
print("_"*40)
print(test_df[test_df.MisAge == 0].Sex.value_counts... | Titanic - Machine Learning from Disaster |
624,751 | if cfg['combine']:
pd.options.display.max_columns=305
paths = [
"submission_1.csv",
"submission_2.csv",
]
weights = [0.2, 0.8]
conf_cols = np.array(["conf_0", "conf_1", "conf_2"])
xy_cols = [[],[],[]]
for i in range(50):
for j in range(3):
xy_cols[j].append(f"coord_x{j}{i}")
xy_cols[j].append(f"coord_y{j}{i}")
xy_co... | for df in combine:
df['Title'] = df.Name.str.extract('([A-Za-z]+)\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'] ) | Titanic - Machine Learning from Disaster |
624,751 | warnings.filterwarnings("ignore" )<import_modules> | for df in combine:
df['Title'] = df['Title'].replace(['Mme', 'Countess','Dona'], 'Mrs')
df['Title'] = df['Title'].replace(['Capt', 'Col','Don', 'Jonkheer', 'Rev',
'Major', 'Sir'], 'Mr')
df['Title'] = df['Title'].replace(['Mlle', 'Lady','Ms'], 'Miss')
df.loc[(df.Sex == 'male')&(df.Title == 'Dr'), 'Title'] = 'Mr'
df.l... | Titanic - Machine Learning from Disaster |
624,751 | l5kit.__version__<set_options> | fil =(test_df.Age.isnull())
print("By title:")
print(test_df[fil].Title.value_counts())
print("_"*40)
print(test_df[test_df.MisAge == 0].Title.value_counts())
print("_"*40)
print("_"*40)
fil =(train_df.Age.isnull())
print("By class:")
print(train_df[fil].Title.value_counts())
print("_"*40)
print(train_df[tra... | Titanic - Machine Learning from Disaster |
624,751 | def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
set_seed(42 )<init_hyperparams> | np.random.seed(452 ) | Titanic - Machine Learning from Disaster |
624,751 | cfg = {
'format_version': 4,
'data_path': "/kaggle/input/lyft-motion-prediction-autonomous-vehicles",
'model_params': {
'model_architecture': 'resnet34',
'history_num_frames': 10,
'history_step_size': 1,
'history_delta_time': 0.5,
'future_num_frames': 50,
'future_step_size': 1,
'future_delta_time': 0.5,
'model_name': "... | for df in combine:
titles = list(set(df.Title))
classes = list(set(df.Pclass))
for title in titles:
for cl in classes:
fil =(df.Title == title)&(df.Pclass == cl)
med_age = df[fil].Age.dropna().median()
var_age = med_age / 5
mis_age = df[fil].MisAge.sum()
df.loc[fil &(df.Age.isnull()), 'Age'] = np.random.randint(int(me... | Titanic - Machine Learning from Disaster |
624,751 | DIR_INPUT = cfg["data_path"]
os.environ["L5KIT_DATA_FOLDER"] = DIR_INPUT
dm = LocalDataManager(None )<create_dataframe> | for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'male':1 , 'female':2} ).astype(int)
train_df.sample(5 ) | Titanic - Machine Learning from Disaster |
624,751 | train_cfg = cfg["train_data_loader"]
rasterizer = build_rasterizer(cfg, dm)
train_zarr = ChunkedDataset(dm.require(train_cfg["key"])).open()
train_dataset = AgentDataset(cfg, train_zarr, rasterizer)
train_dataloader = DataLoader(train_dataset, shuffle=train_cfg["shuffle"], batch_size=train_cfg["batch_size"],
num_work... | for dataset in combine:
dataset['Embarked'] = dataset['Embarked'].map({'S':1 , 'C':2, 'Q':3} ).astype(int)
train_df.sample(5 ) | Titanic - Machine Learning from Disaster |
624,751 | test_cfg = cfg["test_data_loader"]
rasterizer = build_rasterizer(cfg, dm)
test_zarr = ChunkedDataset(dm.require(test_cfg["key"])).open()
test_mask = np.load(f"{DIR_INPUT}/scenes/mask.npz")["arr_0"]
test_dataset = AgentDataset(cfg, test_zarr, rasterizer, agents_mask=test_mask)
test_dataloader = DataLoader(test_dataset... | train_df[['Title', 'Survived']].groupby(['Title'], as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | class LyftMultiModel(nn.Module):
def __init__(self, cfg: Dict, num_modes=3):
super().__init__()
architecture = cfg["model_params"]["model_architecture"]
backbone = eval(architecture )(pretrained=True, progress=True)
self.backbone = backbone
num_history_channels =(cfg["model_params"]["history_num_frames"] + 1)* 2
num_i... | train_df.Title.value_counts() | Titanic - Machine Learning from Disaster |
624,751 | def forward(data, model, device, criterion = pytorch_neg_multi_log_likelihood_batch):
inputs = data["image"].to(device)
target_availabilities = data["target_availabilities"].to(device)
targets = data["target_positions"].to(device)
preds, confidences = model(inputs)
loss = criterion(targets, preds, confidences, targ... | for df in combine:
df['Title'] = df['Title'].map({'Mr':1 , 'Mrs':2, 'Miss':3, 'Master':4} ).astype(int)
train_df.sample(5 ) | Titanic - Machine Learning from Disaster |
624,751 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = LyftMultiModel(cfg)
weight_path = cfg["model_params"]["weight_path"]
if weight_path:
model.load_state_dict(torch.load(weight_path))
model.to(device)
optimizer = optim.Adam(model.parameters() , lr=cfg["model_params"]["lr"])
print(f'devic... | for df in combine:
df['IsAlone'] = 0
fil =(df.SibSp == 0)&(df.Parch == 0)
df.loc[fil, 'IsAlone'] = 1
print(train_df.IsAlone.value_counts())
print("_"*40)
print(test_df.IsAlone.value_counts() ) | Titanic - Machine Learning from Disaster |
624,751 | print(model )<init_hyperparams> | train_df[['IsAlone', 'Survived']].groupby(['IsAlone'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | if cfg["model_params"]["train"]:
tr_it = iter(train_dataloader)
progress_bar = tqdm(range(cfg["train_params"]["max_num_steps"]))
num_iter = cfg["train_params"]["max_num_steps"]
losses_train = []
iterations = []
metrics = []
times = []
model_name = cfg["model_params"]["model_name"]
start = time.time()
for i in progress... | for dataset in combine:
df = dataset.groupby(['Sex', 'IsAlone'] ).size().unstack(0)
df['fem_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]]))
df['male_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]]))
print(df)
print("_"*40 ) | Titanic - Machine Learning from Disaster |
624,751 | pred_path = 'submission1.csv'
write_pred_csv(pred_path,
timestamps=np.concatenate(timestamps),
track_ids=np.concatenate(agent_ids),
coords=np.concatenate(future_coords_offsets_pd),
confs = np.concatenate(confidences_list)
)<import_modules> | train_df[["Sex", "IsAlone", "Survived"]].groupby(['IsAlone','Sex'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | import pandas as pd, numpy as np<load_from_csv> | for dataset in combine:
df = dataset.groupby(['Pclass', 'IsAlone'] ).size().unstack(0)
df['first_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]] + df[df.columns[2]]))
df['second_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]] + df[df.columns[2]]))
df['third_perc'] =(df[df.columns[2]... | Titanic - Machine Learning from Disaster |
624,751 | df = pd.read_csv("./submission1.csv" )<create_dataframe> | train_df[["Pclass", "IsAlone", "Survived"]].groupby(['IsAlone', 'Pclass'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | df_issa1 = df.copy()<set_options> | for df in combine:
df['IsKid'] = 0
fil =(df.Age < 16)
df.loc[fil, 'IsKid'] = 1
print(train_df.IsKid.value_counts())
print("_"*40)
print(test_df.IsKid.value_counts() ) | Titanic - Machine Learning from Disaster |
624,751 | pd.options.display.max_columns=305<load_from_csv> | train_df[['IsKid', 'Survived']].groupby(['IsKid'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | df = pd.read_csv(".. /input/lyft-test-set-as-csv/Lyft_test_set.csv")
print("df.shape:", df.shape)
df.head(10 )<feature_engineering> | bins = [0, 16, 32, 48, 81]
for df in combine:
df['AgeBin'] = pd.cut(df['Age'], bins)
print(train_df.AgeBin.value_counts())
print("_"*40)
print(test_df.AgeBin.value_counts() ) | Titanic - Machine Learning from Disaster |
624,751 | def get_models(path):
models = {}
path = Path(path)
for model in path.glob("lgbm*"):
model_name = get_model_name(model.stem)
shift = int(model_name.split("shift_")[1])
meta = path.joinpath("meta_shift_{:02d}.json".format(shift))
with meta.open() as f:
train_cols = json.load(f)["TRAIN_COLS"]
models[model_name] = {"mo... | train_df[['AgeBin', 'Survived']].groupby(['AgeBin'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | models = get_models(".. /input/lyft-models/lgbm_06")
len(models )<define_variables> | bins = [0, 16, 32, 48, 81]
names = [0, 1, 2, 3]
for df in combine:
df['AgeBin'] = pd.cut(df['Age'], bins, labels = names)
df['AgeBin'] = pd.to_numeric(df['AgeBin'])
print(train_df.AgeBin.value_counts())
print("_"*40)
print(test_df.AgeBin.value_counts() ) | Titanic - Machine Learning from Disaster |
624,751 | def make_colnames() :
xcols = ["coord_x{}{}".format(step, rank)for step in range(3)for rank in range(50)]
ycols = ["coord_y{}{}".format(step, rank)for step in range(3)for rank in range(50)]
cols = ["timestamp", "track_id"] + ["conf_0", "conf_1", "conf_2"] + list(it.chain(*zip(xcols, ycols)))
return cols<prepare_output... | for df in combine:
df['FareCat'] = pd.qcut(df.FarePP, 4)
print(train_df.FareCat.value_counts())
print("_"*40)
print(test_df.FareCat.value_counts() ) | Titanic - Machine Learning from Disaster |
624,751 | def predict(models, df):
sub = np.empty(( len(df), 305))
sub.fill(np.nan)
sub = pd.DataFrame(sub, columns = make_colnames())
sub[["timestamp", "track_id"]] = df[["timestamp", "track_id"]]
sub["conf_0"] = 1.0
for shift in range(1, 51):
for suffix in ["x", "y"]:
model_info = models["lgbm_{}_shift_{:02d}".format(suffix,... | labels = [0, 1, 2, 3]
for df in combine:
df['FareCat'] = pd.qcut(df.FarePP, 4, labels=labels)
df['FareCat'] = pd.to_numeric(df['FareCat'])
print(train_df.FareCat.value_counts())
print("_"*40)
print(test_df.FareCat.value_counts() ) | Titanic - Machine Learning from Disaster |
624,751 | sub = predict(models, df )<save_to_csv> | train_df[['FareCat', 'Survived']].groupby(['FareCat'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | sub.to_csv("submission2.csv", index=False )<create_dataframe> | for dataset in combine:
df = dataset.groupby(['Pclass', 'FareCat'] ).size().unstack(0 ).fillna(0)
df['first_perc'] =(df[df.columns[0]]/(df[df.columns[0]] + df[df.columns[1]] + df[df.columns[2]]))
df['second_perc'] =(df[df.columns[1]]/(df[df.columns[0]] + df[df.columns[1]] + df[df.columns[2]]))
df['third_perc'] =(df[df... | Titanic - Machine Learning from Disaster |
624,751 | df_issa2 = sub.copy()<import_modules> | for df in combine:
df['FamSize'] = 0
df.loc[(df.NumFam > 1), 'FamSize'] = 1
df.loc[(df.NumFam > 3), 'FamSize'] = 2
df.loc[(df.NumFam > 5), 'FamSize'] = 3
print(train_df.FamSize.value_counts())
print("_"*40)
print(test_df.FamSize.value_counts() ) | Titanic - Machine Learning from Disaster |
624,751 | import pandas as pd, numpy as np<save_to_csv> | train_df[['FamSize', 'Survived']].groupby(['FamSize'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | df_issa1.to_csv('df_issa1.csv', index=False)
df_issa2.to_csv('df_issa2.csv', index=False )<feature_engineering> | train_df[["Pclass", "FamSize", "Survived"]].groupby(['FamSize', 'Pclass'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | x = 0.7
df = df_issa1.copy()
df['timestamp'] = x*df_issa1['timestamp'] +(1-x)*df_issa2['timestamp']
df['track_id'] = x*df_issa1['track_id'] +(1-x)*df_issa2['track_id']
df.head()<save_to_csv> | train_df[["Sex", "FamSize", "Survived"]].groupby(['FamSize', 'Sex'],
as_index=True ).mean() | Titanic - Machine Learning from Disaster |
624,751 | df.to_csv('submission.csv', index=False )<load_pretrained> | for df in combine:
df['Se_Cl'] = 0
df.loc[(( df.Sex == 1)&(df.Pclass == 1)) , 'Se_Cl'] = 1
df.loc[(( df.Sex == 1)&(df.Pclass == 2)) , 'Se_Cl'] = 2
df.loc[(( df.Sex == 1)&(df.Pclass == 3)) , 'Se_Cl'] = 3
df.loc[(( df.Sex == 2)&(df.Pclass == 1)) , 'Se_Cl'] = 4
df.loc[(( df.Sex == 2)&(df.Pclass == 2)) , 'Se_Cl'] = 5
df.lo... | Titanic - Machine Learning from Disaster |
624,751 | if not os.path.exists('/kaggle/input/lyft-motion-prediction-autonomous-vehicles'):
drive.mount('/content/drive')
!pip install -q kaggle
if not os.path.exists('kaggle.json'):
files.upload()
!mkdir ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
!kaggle config path -p /content
def kaggle_dataset_do... | for df in combine:
df['Cl_IA'] = 0
df.loc[(( df.IsAlone == 1)&(df.Pclass == 1)) , 'Cl_IA'] = 1
df.loc[(( df.IsAlone == 1)&(df.Pclass == 2)) , 'Cl_IA'] = 2
df.loc[(( df.IsAlone == 1)&(df.Pclass == 3)) , 'Cl_IA'] = 3
df.loc[(( df.IsAlone == 0)&(df.Pclass == 1)) , 'Cl_IA'] = 4
df.loc[(( df.IsAlone == 0)&(df.Pclass == 2)) ... | Titanic - Machine Learning from Disaster |
624,751 | !pip install -q l5kit
warnings.filterwarnings("ignore" )<import_modules> | for df in combine:
df['Ca_Cl'] = 0
df.loc[(( df.Cabin == 0)&(df.Pclass == 1)) , 'Ca_Cl'] = 1
df.loc[(( df.Cabin == 0)&(df.Pclass == 2)) , 'Ca_Cl'] = 2
df.loc[(( df.Cabin == 0)&(df.Pclass == 3)) , 'Ca_Cl'] = 3
df.loc[(( df.Cabin == 1)&(df.Pclass == 1)) , 'Ca_Cl'] = 4
df.loc[(( df.Cabin == 1)&(df.Pclass == 2)) , 'Ca_Cl']... | Titanic - Machine Learning from Disaster |
624,751 | l5kit.__version__<set_options> | for df in combine:
df['MA_Cl'] = 0
df.loc[(( df.MisAge == 0)&(df.Pclass == 1)) , 'MA_Cl'] = 1
df.loc[(( df.MisAge == 0)&(df.Pclass == 2)) , 'MA_Cl'] = 2
df.loc[(( df.MisAge == 0)&(df.Pclass == 3)) , 'MA_Cl'] = 3
df.loc[(( df.MisAge == 1)&(df.Pclass == 1)) , 'MA_Cl'] = 4
df.loc[(( df.MisAge == 1)&(df.Pclass == 2)) , 'MA... | Titanic - Machine Learning from Disaster |
624,751 | def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
set_seed(42 )<init_hyperparams> | for df in combine:
df['IK_Cl'] = 0
df.loc[(( df.IsKid == 0)&(df.Pclass == 1)) , 'IK_Cl'] = 1
df.loc[(( df.IsKid == 0)&(df.Pclass == 2)) , 'IK_Cl'] = 2
df.loc[(( df.IsKid == 0)&(df.Pclass == 3)) , 'IK_Cl'] = 3
df.loc[(( df.IsKid == 1)&(df.Pclass == 1)) , 'IK_Cl'] = 4
df.loc[(( df.IsKid == 1)&(df.Pclass == 2)) , 'IK_Cl']... | Titanic - Machine Learning from Disaster |
624,751 | cfg = {
'format_version': 4,
'data_path': data_path,
'model_params': {
'model_architecture': 'resnet34',
'history_num_frames': 10,
'history_step_size': 1,
'history_delta_time': 0.1,
'future_num_frames': 50,
'future_step_size': 1,
'future_delta_time': 0.1,
'model_name': "model_resnet34_output",
'lr': 1e-3,
'weight_path'... | for df in combine:
df["Em_Cl"] = df["Embarked"] * df["Pclass"]
print(train_df.Em_Cl.value_counts())
print("_"*40)
print(test_df.Em_Cl.value_counts())
print("_"*40)
print("_"*40)
train_df[['Em_Cl', 'Survived']].groupby(['Em_Cl'],
as_index=True ).mean().sort_values(by='Survived',
ascending=False ) | Titanic - Machine Learning from Disaster |
624,751 | DIR_INPUT = cfg["data_path"]
os.environ["L5KIT_DATA_FOLDER"] = DIR_INPUT
dm = LocalDataManager(None )<create_dataframe> | for df in combine:
df['Se_Ca'] = 0
df.loc[(( df.Sex == 1)&(df.Cabin == 0)) , 'Se_Ca'] = 1
df.loc[(( df.Sex == 1)&(df.Cabin == 1)) , 'Se_Ca'] = 2
df.loc[(( df.Sex == 2)&(df.Cabin == 0)) , 'Se_Ca'] = 3
df.loc[(( df.Sex == 2)&(df.Cabin == 1)) , 'Se_Ca'] = 4
print(train_df.Se_Ca.value_counts())
print("_"*40)
print(test_d... | Titanic - Machine Learning from Disaster |
624,751 | train_cfg = cfg["train_data_loader"]
rasterizer = build_rasterizer(cfg, dm)
train_zarr = ChunkedDataset(dm.require(train_cfg["key"])).open()
train_dataset = AgentDataset(cfg, train_zarr, rasterizer)
train_dataloader = DataLoader(train_dataset, shuffle=train_cfg["shuffle"], batch_size=train_cfg["batch_size"],
num_work... | for df in combine:
df['MA_Ca'] = 0
df.loc[(( df.MisAge == 0)&(df.Cabin == 0)) , 'MA_Ca'] = 1
df.loc[(( df.MisAge == 0)&(df.Cabin == 1)) , 'MA_Ca'] = 2
df.loc[(( df.MisAge == 1)&(df.Cabin == 0)) , 'MA_Ca'] = 3
df.loc[(( df.MisAge == 1)&(df.Cabin == 1)) , 'MA_Ca'] = 4
print(train_df.MA_Ca.value_counts())
print("_"*40)
... | Titanic - Machine Learning from Disaster |
624,751 | test_cfg = cfg["test_data_loader"]
rasterizer = build_rasterizer(cfg, dm)
test_zarr = ChunkedDataset(dm.require(test_cfg["key"])).open()
test_mask = np.load(f"{DIR_INPUT}/scenes/mask.npz")["arr_0"]
test_dataset = AgentDataset(cfg, test_zarr, rasterizer, agents_mask=test_mask)
test_dataloader = DataLoader(test_dataset... | for df in combine:
df['Se_IA'] = 0
df.loc[(( df.Sex == 1)&(df.IsAlone == 0)) , 'Se_IA'] = 1
df.loc[(( df.Sex == 1)&(df.IsAlone == 1)) , 'Se_IA'] = 2
df.loc[(( df.Sex == 2)&(df.IsAlone == 0)) , 'Se_IA'] = 3
df.loc[(( df.Sex == 2)&(df.IsAlone == 1)) , 'Se_IA'] = 4
print(train_df.Se_IA.value_counts())
print("_"*40)
prin... | Titanic - Machine Learning from Disaster |
624,751 | class LyftMultiModel(nn.Module):
def __init__(self, cfg: Dict, num_modes=3):
super().__init__()
architecture = cfg["model_params"]["model_architecture"]
backbone = eval(architecture )(pretrained=True, progress=True)
self.backbone = backbone
num_history_channels =(cfg["model_params"]["history_num_frames"] + 1)* 2
num_i... | warnings.filterwarnings("ignore" ) | Titanic - Machine Learning from Disaster |
624,751 | def forward(data, model, device, criterion = pytorch_neg_multi_log_likelihood_batch):
inputs = data["image"].to(device)
target_availabilities = data["target_availabilities"].to(device)
targets = data["target_positions"].to(device)
preds, confidences = model(inputs)
loss = criterion(targets, preds, confidences, targ... | features = ['Pclass', 'Sex', 'Cabin', 'Embarked', 'Title', 'AgeBin', 'MisAge', 'IsKid',
'FamSize', 'Se_Cl', 'Cl_IA', 'Se_Ca', 'MA_Ca', 'Se_IA']
y = train_df['Survived'].copy()
X = train_df[features].copy()
test = test_df[features].copy()
X.head() | Titanic - Machine Learning from Disaster |
624,751 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = LyftMultiModel(cfg)
print(torch.cuda.is_available())
weight_path = cfg["model_params"]["weight_path"]
if weight_path:
model.load_state_dict(torch.load(weight_path))
model.to(device)
optimizer = optim.Adam(model.parameters() , lr=cfg["mo... | clf_list = [DecisionTreeClassifier() ,
RandomForestClassifier() ,
AdaBoostClassifier() ,
GradientBoostingClassifier() ,
XGBClassifier() ,
Perceptron() ,
LogisticRegression() ,
SVC() ,
LinearSVC() ,
KNeighborsClassifier() ,
GaussianNB() ,
SGDClassifier()
] | Titanic - Machine Learning from Disaster |
624,751 | print(model )<init_hyperparams> | mdl = []
bias_acc = []
var_acc = []
bias_f1 = []
var_f1 = []
bias_auc = []
var_auc = []
acc_scorer = make_scorer(f1_score)
for clf in clf_list:
model = clf.__class__.__name__
res = cross_val_score(clf, X, y, scoring='accuracy', cv = 5)
score = round(res.mean() * 100, 3)
var = round(res.std() , 3)
bias_acc.append(sc... | Titanic - Machine Learning from Disaster |
624,751 | if cfg["model_params"]["train"]:
tr_it = iter(train_dataloader)
progress_bar = tqdm(range(cfg["train_params"]["max_num_steps"]))
num_iter = cfg["train_params"]["max_num_steps"]
losses_train = []
iterations = []
metrics = []
times = []
model_name = cfg["model_params"]["model_name"]
start = time.time()
for i in progress... | print("Best for accuracy")
print(robcon[['Model','Bias_acc']].sort_values(by= 'Bias_acc', ascending=False ).head(6))
print("_"*40)
print("Best for f1")
print(robcon[['Model','Bias_f1']].sort_values(by= 'Bias_f1', ascending=False ).head(6))
print("_"*40)
print("Best for roc_auc")
print(robcon[['Model','Bias_auc']].... | Titanic - Machine Learning from Disaster |
624,751 | pred_path = 'submission.csv'
write_pred_csv(pred_path,
timestamps=np.concatenate(timestamps),
track_ids=np.concatenate(agent_ids),
coords=np.concatenate(future_coords_offsets_pd),
confs = np.concatenate(confidences_list)
)<import_modules> | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=895 ) | Titanic - Machine Learning from Disaster |
624,751 | import numpy as np
import pandas as pd
import os
<save_to_csv> | from sklearn.feature_selection import RFECV | Titanic - Machine Learning from Disaster |
624,751 | SUBMISSION_FOLDER = '.. /input/lyft-submit'
SUBMISSION_FILE = 'ext_submission.csv'
submissions = pd.read_csv(f"{SUBMISSION_FOLDER}/{SUBMISSION_FILE}")
submissions.to_csv("submission.csv", index=False )<set_options> | FeatSel_log = RFECV(LogisticRegression() , step = 1, scoring = 'roc_auc', cv = 10)
FeatSel_log.fit(X_train, y_train)
BestFeat_log = X_train.columns.values[FeatSel_log.get_support() ]
BestFeat_log | Titanic - Machine Learning from Disaster |
624,751 | pd.set_option('max_columns', 50 )<import_modules> | param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000],
'tol': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1],
'random_state' : [42]}
grid_log = GridSearchCV(LogisticRegression() , param_grid, cv = 10, scoring= 'roc_auc')
%time grid_log.fit(X_train[BestFeat_log], y_train)
best_log = grid_log.best_estimator_... | Titanic - Machine Learning from Disaster |
624,751 | print("l5kit version:", l5kit.__version__ )<import_modules> | param_grid = {'C': np.arange(1,10),
'tol': [0.0001, 0.001, 0.01, 0.1, 1],
'kernel': ['linear', 'rbf', 'poly', 'sigmoid'],
'random_state': [42]}
grid_SVC = GridSearchCV(SVC() , param_grid, cv = 10, scoring= 'roc_auc')
%time grid_SVC.fit(X_train, y_train)
best_SVC = grid_SVC.best_estimator_
print(best_SVC)
print("_"*4... | Titanic - Machine Learning from Disaster |
624,751 | import torch
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Subset
from torchvision.models import resnet18
from torch import nn
from typing import Dict<choose_model_class> | FeatSel_ada = RFECV(AdaBoostClassifier() , step = 1, scoring = 'roc_auc', cv = 10)
FeatSel_ada.fit(X_train, y_train)
BestFeat_ada = X_train.columns.values[FeatSel_ada.get_support() ]
BestFeat_ada | Titanic - Machine Learning from Disaster |
624,751 | class LyftMultiModel(nn.Module):
def __init__(self, cfg: Dict, num_modes=3):
super().__init__()
backbone = resnet18(pretrained=False, progress=True)
self.backbone = backbone
num_history_channels =(cfg["model_params"]["history_num_frames"] + 1)* 2
num_in_channels = 3 + num_history_channels
self.backbone.conv1 = nn.Conv... | param_grid = {'n_estimators': np.arange(50, 500, 50),
'learning_rate': [0.0001, 0.001, 0.01, 0.1, 1, 2],
'algorithm': ['SAMME', 'SAMME.R'],
'random_state': [42]}
grid_ada = GridSearchCV(AdaBoostClassifier() , param_grid, cv = 10, scoring= 'roc_auc')
%time grid_ada.fit(X_train[BestFeat_ada], y_train)
best_ada = grid_a... | Titanic - Machine Learning from Disaster |
624,751 | def save_yaml(filepath, content, width=120):
with open(filepath, 'w')as f:
yaml.dump(content, f, width=width)
def load_yaml(filepath):
with open(filepath, 'r')as f:
content = yaml.safe_load(f)
return content
class DotDict(dict):
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
<... | FeatSel_for = RFECV(RandomForestClassifier() , step = 1, scoring = 'roc_auc', cv = 10)
FeatSel_for.fit(X_train, y_train)
BestFeat_for = X_train.columns.values[FeatSel_for.get_support() ]
BestFeat_for | Titanic - Machine Learning from Disaster |
624,751 | def run_prediction(predictor, data_loader):
predictor.eval()
pred_coords_list = []
confidences_list = []
timestamps_list = []
track_id_list = []
with torch.no_grad() :
dataiter = tqdm(data_loader)
for data in dataiter:
image = data["image"].to(device)
pred, confidences = predictor(image)
pred_coords_list.append(pred... | param_grid = {'n_estimators': np.arange(10, 100, 10),
'max_depth': np.arange(2,20),
'max_features' : ['auto', 'log2', None],
'criterion' : ['gini', 'entropy'],
'random_state' : [42]}
grid_forest = GridSearchCV(RandomForestClassifier() , param_grid, cv = 10, scoring= 'roc_auc')
%time grid_forest.fit(X_train[BestFeat_fo... | Titanic - Machine Learning from Disaster |
624,751 | cfg = {
'format_version': 4,
'model_params': {
'model_architecture': 'resnet50',
'history_num_frames': 10,
'history_step_size': 1,
'history_delta_time': 0.1,
'future_num_frames': 50,
'future_step_size': 1,
'future_delta_time': 0.1
},
'raster_params': {
'raster_size': [448, 448],
'pixel_size': [0.5, 0.5],
'ego_center': ... | FeatSel_XGB = RFECV(XGBClassifier() , step = 1, scoring = 'roc_auc', cv = 10)
FeatSel_XGB.fit(X_train, y_train)
BestFeat_XGB = X_train.columns.values[FeatSel_XGB.get_support() ]
BestFeat_XGB | Titanic - Machine Learning from Disaster |
624,751 | flags_dict = {
"debug": False,
"l5kit_data_folder": "/kaggle/input/lyft-motion-prediction-autonomous-vehicles",
"pred_mode": "multi",
"device": "cuda:0",
"out_dir": "results/multi_train",
"epoch": 2,
"snapshot_freq": 50,
}<load_pretrained> | param_grid = {'learning_rate': [0.0001, 0.001, 0.01, 0.1, 1, 2],
'max_depth': np.arange(2,10),
'n_estimators': np.arange(50, 500, 50),
'random_state': [42]}
grid_XGB = GridSearchCV(XGBClassifier() , param_grid, cv = 10, scoring= 'roc_auc')
%time grid_XGB.fit(X_train[BestFeat_XGB], y_train)
best_XGB = grid_XGB.best_es... | Titanic - Machine Learning from Disaster |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.