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values | graph_vertex_subclass stringclasses 70
values | kernel_id float64 25.8k 14.7M ⌀ | sqale_rating float64 1 4 | maintainability_rating stringclasses 4
values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
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values | context_line_count int64 0 33 | combined_rating stringclasses 5
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,259 | sum(train_data['Sales'] < 0 ) | No | 5 | 72 | EDA | count_values | 3,237,641 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
2,260 | train_data.head(100) | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,261 | categorical_train = train_data.columns.tolist()
print(categorical_train)
train_data[categorical_train].corr(method='pearson') | No | 3 | 80 | Visualization | heatmap | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
2,262 | train_features = train_data.drop(['Open'],axis = 1)
categorical_train = train_features.columns.tolist()
print(categorical_train)
train_data[categorical_train].corr(method='pearson')
train_features = train_data.drop(['Sales'],axis = 1)
full_features = pd.concat([train_features,test_data],ignore_index= True)
print(train_... | No | 2 | 80 | Visualization | heatmap | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
2,263 | full_features.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,264 | full_features.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,265 | full_features = pd.get_dummies(full_features,columns= ['HolidayBin','Assortment','StoreType']) | No | 5 | 20 | Data_Transform | categorify | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,266 | full_features = full_features.drop('PromoInterval',axis = 1) | No | 5 | 10 | Data_Transform | drop_column | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,267 | train_features = full_features.iloc[:844392,:].values
test_data = full_features.iloc[844392:,:].values
train_sales = train_data['Sales'].values | No | 5 | 13 | Data_Transform | split | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
2,268 | print(train_features.shape)
print(train_sales.shape)
print(test_data.shape) | No | 5 | 58 | Debug | show_shape | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,270 | xgboost = XGBRegressor(learning_rate=0.009, n_estimators=500,
max_depth=10, min_child_weight=0,
gamma=0, subsample=0.7,
colsample_bytree=0.7,
objective='reg:linear', nthrea... | No | 5 | 4 | Model_Train | choose_model_class | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,271 | xgboost.fit(train_features,train_sales)
| No | 5 | 7 | Model_Train | train_model | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,272 | predictions = xgboost.predict(test_data) | No | 5 | 48 | Model_Evaluation | predict_on_test | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,274 | pred_df = pd.DataFrame({"Id": test_copy["Id"], 'Sales': predictions})
pred_df.to_csv("xgboost_4_submission.csv", index=False)' | No | 4 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,275 | import os
import string
import numpy as np
import pandas as pd
from pandasql import sqldf
import matplotlib.pyplot as plt
from keras.utils.np_utils import to_categorical
from keras.models import Model, Sequential, model_from_json
from keras.optimizers import SGD, Adam, RMSprop
from keras.layers import Input, Dense, D... | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
2,276 | b"def concat_data():\n df_train = pd.read_csv('../input/train.csv')\n df_test = pd.read_csv('../input/test.csv')\n df_extra = pd.read_csv('../input/store.csv')\n df_test['Sales'] = -1\n df_full = pd.concat([df_train, df_test]).reset_index(drop=True)\n\n #Merge extra information about stores\n df_fu... | Yes | 2 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,277 | def extrat_test_data(df_full):
df_train = df_full.loc[df_full['Sales'] != -1]
df_test = df_full.loc[df_full['Sales'] == -1]
return df_train, df_test
df_train, df_test = extrat_test_data(df_full) | No | 4 | 13 | Data_Transform | split | 3,674,829 | 1 | A | 0 | 1 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,278 | df_full.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,279 | # Function to calculate missing values by column (By DSA)
def missing_values_table(df):
# Total missing values
mis_val = df.isnull().sum()
# Percentage of missing values
mis_val_percent = 100 * df.isnull().sum() / len(df)
# Make a table with the results
mis_val_table = pd.concat([mis_val, mis_... | No | 4 | 39 | EDA | count_missing_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,281 | df_train_store.dtypes | No | 5 | 70 | Debug | show_data_types | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,282 | df_full.groupby('StoreType')['Sales'].describe() | No | 5 | 40 | Debug | show_table_attributes | 3,674,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,283 | df_full.groupby('StoreType')['Customers', 'Sales'].sum() | No | 5 | 60 | Data_Transform | groupby | 3,674,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,284 | # Plotting correlations
num_feat=df_full.columns[df_full.dtypes!=object]
num_feat=num_feat[1:-1]
labels = []
values = []
for col in num_feat:
labels.append(col)
values.append(np.corrcoef(df_full[col].values, df_full['Sales'].values)[0,1])
ind = np.arange(len(labels))
width = 0.9
fig, ax = plt.subplots(fig... | No | 3 | 80 | Visualization | heatmap | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,285 | # Heatmap of correlations features
corrMatrix=df_full[["Sales", "DayOfWeek", "Open", "Promo", "SchoolHoliday", "CompetitionDistance",
"CompetitionOpenSinceMonth", "CompetitionOpenSinceYear", "Promo2",
"Promo2SinceWeek", "Promo2SinceYear", "Year", "Month", "Day",
... | No | 4 | 80 | Visualization | heatmap | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,286 | def clean_data(use_text_columns = True):
'''
Function that clean data and create a new features to enrich the model
'''
cols_num = ["Sales", "DayOfWeek", "Open", "Promo", "SchoolHoliday", "CompetitionDistance",
"CompetitionOpenSinceMonth", "CompetitionOpenSinceYear", "Promo2",
... | Yes | 3 | 43 | Data_Extraction | load_from_sql | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,287 | def load_train_data(scaler_x, scaler_y):
'''
Transform train data set and separate a test dataset to validate the model in the end of training and normalize data
'''
X_train = train.drop(["Sales"], axis=1) # Features
y_train = np.array(train["Sales"]).reshape((len(X_train), 1)) # Targets
X_train... | No | 3 | 13 | Data_Transform | split | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,288 | def load_test_data():
'''
Remove column of predictions and normalize data of submission test data set.
'''
X_test = test.drop(["Sales"], axis=1) # Features
X_test = StandardScaler().fit_transform(X_test)
return X_test' | No | 4 | 20 | Data_Transform | categorify | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,289 | b"# Show info of model\ndef show_info(model, X, y, log, weights = None):\n '''\n Show metrics about the evaluation model and plots about loss, rmse and rmspe\n '''\n if (log != None):\n # summarize history for loss\n plt.figure(figsize=(14,10))\n plt.plot(log.history['loss'])\n p... | Yes | 3 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,290 | def rmspe_val(y_true, y_pred):
'''
RMSPE calculus to validate evaluation metric about the model
'''
return np.sqrt(np.mean(np.square((y_true - y_pred) / y_true), axis=0))[0] | No | 5 | 84 | Other | not_enough_vertices | 3,674,829 | 1 | A | 0 | 1 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,291 | def rmspe(y_true, y_pred):
'''
RMSPE calculus to use during training phase
'''
return K.sqrt(K.mean(K.square((y_true - y_pred) / y_true), axis=-1)) | No | 5 | 84 | Other | not_enough_vertices | 3,674,829 | 1 | A | 0 | 1 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,292 | def rmse(y_true, y_pred):
'''
RMSE calculus to use during training phase
'''
return K.sqrt(K.mean(K.square(y_pred - y_true))) | No | 5 | 84 | Other | not_enough_vertices | 3,674,829 | 1 | A | 0 | 1 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,293 | def create_model():
'''
Create a neural network
'''
initializer = RandomNormal(mean=0.0, stddev=0.05, seed=None)
model = Sequential()
model.add(Dense(512, input_dim=X_train.shape[1], activation="relu", kernel_initializer=initializer))
model.add(Dropout(0.4))
model.add(Dense(512, input_d... | No | 3 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,294 | train, test = clean_data(use_text_columns = True) | No | 3 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,295 | # Hyperparameters and load data to train the model
batch_size = 512
nb_epoch = 300
scaler_x = StandardScaler()
scaler_y = StandardScaler()
print('Loading data...')
(X_train, y_train), (X_test, y_test) = load_train_data(scaler_x, scaler_y)
print('Build model...')
model = create_model()
model.summary() | Yes | 3 | 4 | Model_Train | choose_model_class | 3,674,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,296 | print('Fit model...')
filepath="weights_rossmann.best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='min')
callbacks_list = [checkpoint, early_stopping]
log = model.fit(X_train... | No | 4 | 7 | Model_Train | train_model | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,297 | show_info(model, X_test, y_test, log, weights='weights_rossmann.best.hdf5') | No | 4 | 35 | Visualization | learning_history | 3,674,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,298 | test_data = load_test_data()
df_teste = pd.read_csv('../input/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 3,674,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,299 | predict = model.predict(test_data)
predict = scaler_y.inverse_transform(predict) | No | 4 | 48 | Model_Evaluation | predict_on_test | 3,674,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,300 | submission = pd.DataFrame()
submission['Id'] = df_teste["Id"]
submission['Sales'] = predict
submission.to_csv('submission.csv', index=False)' | No | 4 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,301 | df_correlation=df_train_store[['Store', 'DayOfWeek', 'Sales', 'Customers', 'Open', 'Promo',
'SchoolHoliday',
'CompetitionDistance', 'CompetitionOpenSinceMonth',
'CompetitionOpenSinceYear', 'Promo2', 'Promo2SinceWeek',
'Promo2SinceYear', 'SalesperCustomer', 'Month', 'Year',
'Day', 'St... | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,302 | df_correlation=df_correlation.drop('Open', axis = 1) | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,303 | upper_triangle = np.zeros_like(df_correlation.corr(), dtype = np.bool)
upper_triangle[np.triu_indices_from(upper_triangle)] = True #make sure we don't show half of the other triangle
f, ax = plt.subplots(figsize = (15, 10))
sns.heatmap(df_correlation.corr(),ax=ax,mask=upper_triangle,annot=True, fmt='.2f',linewidths=0.5... | No | 3 | 80 | Visualization | heatmap | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,304 | df_train_store.columns | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,305 | df_train_store['CompetitionOpenSince'] = np.where((df_train_store['CompetitionOpenSinceMonth']==0) & (df_train_store['CompetitionOpenSinceYear']==0) , 0,(df_train_store.Month - df_train_store.CompetitionOpenSinceMonth) +
(12 * (df_train_store.Year - df_train_store.CompetitionOpen... | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,306 | #now that CompetitionOpenSince is created
#we can get rid of `CompetitionOpenSinceYear` and `CompeitionOpenSinceMonth`
del df_train_store['CompetitionOpenSinceYear']
del df_train_store['CompetitionOpenSinceMonth']
| No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,307 | df_train_store["is_holiday_state"] = df_train_store['StateHoliday'].map({"0": 0, "a": 1, "b": 1, "c": 1})' | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,308 | del df_train_store['StateHoliday_cat'] | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,309 | df_train_store=pd.get_dummies(df_train_store, columns=["Assortment", "StoreType","PromoInterval"], prefix=["is_Assortment", "is_StoreType","is_PromoInteval"]) | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,310 | del df_train_store['Assortment_cat']
del df_train_store['StoreType_cat']
| No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,311 | del df_train_store['PromoInterval_cat'] | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,312 | df_test = pd.read_csv("../input/test.csv",sep=',', parse_dates=['Date']
, date_parser=str_to_date,
low_memory = False)
print ("The Test dataset has {} Rows and {} Variables".format(str(df_test.shape[0]),str(df_test.shape[1])))' | No | 3 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,313 | df_test.fillna(1, inplace = True) #11rows with Nans decided to leave them open since its one store 622 which is
#usually open
#Left-join the train to the store dataset since .Why?
#Because you want to make sure you have all events even if some of them don't have their store information ( which shouldn't happen)
df_tes... | Yes | 3 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,314 | del df_test_store["Date"]
del df_test_store['CompetitionOpenSinceYear']
del df_test_store['CompetitionOpenSinceMonth']
' | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,315 | del df_test_store['StateHoliday_cat'] | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,316 | del df_test_store['Assortment_cat']
del df_test_store['StoreType_cat']
del df_test_store['PromoInterval_cat'] | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,317 | del df_test_store['StateHoliday'] | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,318 | del df_train_store['StateHoliday'] | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,319 | def rmspe(y, yhat):
rmspe = np.sqrt(np.mean( (y - yhat)**2 ))
return rmspe | No | 4 | 49 | Model_Evaluation | compute_test_metric | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,320 | features = df_train_store.drop(['Customers', 'Sales', 'SalesperCustomer'], axis = 1)
#a rule of thumb is to transform my target value to log if i see the values are very dispersed which is the case
#and then of course revert them with np.exp to their real values
targets=np.log(df_train_store.Sales)
| Yes | 3 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,321 | X_train, X_train_test, y_train, y_train_test = model_selection.train_test_split(features, targets, test_size=0.20, random_state=15)
print ("Training and testing split was successful.")
| No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,322 | rfr = RandomForestRegressor(n_estimators=10,
criterion='mse',
max_depth=5,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
... | No | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 17 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,323 |
'''
params = {'max_depth':(4,6,8,10,12,14,16,20),
'n_estimators':(4,8,16,24,48,72,96,128),
'min_samples_split':(2,4,6,8,10)}
#scoring_fnc = metrics.make_scorer(rmspe)
#the dimensionality is high, the number of combinations we have to search is enormous, using RandomizedSearchCV
# is a better option ... | No | 3 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,325 | #with the optimal parameters i got let's see how it behaves with the validation set
rfr_val=RandomForestRegressor(n_estimators=128,
criterion='mse',
max_depth=20,
min_samples_split=10,
min_samples_lea... | No | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 17 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,326 | yhat=model_RF_test.predict(X_train_test) | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,327 | plt.hist(yhat) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,328 | error=rmspe(y_train_test,yhat)
error | No | 5 | 49 | Model_Evaluation | compute_test_metric | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,329 | importances = rfr_val.feature_importances_
std = np.std([rfr_val.feature_importances_ for tree in rfr_val.estimators_],
axis=0)
indices = np.argsort(importances)
palette1 = itertools.cycle(sns.color_palette())
# Store the feature ranking
features_ranked=[]
for f in range(X_train.shape[1]):
features_ran... | Yes | 2 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,330 | df_test_store1=df_test_store.drop(['Id'],axis=1)
kaggle_yhat= model_RF_test.predict(df_test_store1)
kaggle_preds= pd.DataFrame({'Id': df_test_store['Id'],
'Sales': np.exp(kaggle_yhat)})
kaggle_preds.to_csv("Stefano_Zakher_RF_Rossman_Kaggle_submission.csv", index = False)
' | Yes | 3 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,331 | import os
import pandas as pd
import numpy as np
import scipy
import warnings
warnings.filterwarnings(action='ignore')
# Plotting Library
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('Solarize_Light2')
# Other Libraries
from sklearn.linear_model import LinearRegression
from sklearn.metrics i... | No | 4 | 88 | Debug | list_files | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
2,332 | def ispromomonth(rows):
# if not rows[0].isnull():
months = {}
months = str(rows['PromoInterval']).split(',')
if str(rows['month_str']) in months:
return 1
else:
return 0
def rmspe(y, yhat):
return np.sqrt(np.mean((yhat/y-1) ** 2))
def rmspe_xg(yhat, y):
y = np.expm1(y.get_lab... | Yes | 1 | 7 | Model_Train | train_model | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,333 | ross = Rossmann_() | No | 5 | 77 | Other | define_variables | 4,029,935 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 16 | A |
2,334 | ross.prepare_sample_data(limit =200 , testing_limit = 40)
ross.Linear_Regression() | No | 4 | 7 | Model_Train | train_model | 4,029,935 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 16 | A |
2,335 | ross.prepare_sample_data(limit =2000 , testing_limit = 400)
ross.Mulitple_Linear_Regression() | No | 4 | 7 | Model_Train | train_model | 4,029,935 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 16 | A |
2,336 | ross.prepare_sample_data(limit =10000 , testing_limit = 4000)
ross.Polynomial_Regression(degrees = 3) | No | 4 | 7 | Model_Train | train_model | 4,029,935 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 16 | A |
2,337 | ross.prepare_sample_data(limit =10000 , testing_limit = 4000)
ross.Polynomial_Regression(degrees = 2) | No | 4 | 7 | Model_Train | train_model | 4,029,935 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 16 | A |
2,338 | ross.prepare_sample_data(limit =1000 , testing_limit = 400)
ross.Other_Regression(reg = 'Ridge') | No | 4 | 7 | Model_Train | train_model | 4,029,935 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 16 | A |
2,339 | ross.prepare_sample_data(limit =1000 , testing_limit = 400)
ross.Other_Regression(reg = 'Lasso') | No | 4 | 7 | Model_Train | train_model | 4,029,935 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 16 | A |
2,340 | ross.prepare_sample_data(limit =1000 , testing_limit = 400)
ross.Other_Regression(reg = 'Elastic') | No | 4 | 7 | Model_Train | train_model | 4,029,935 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 16 | A |
2,341 | ross.prepare_sample_data(limit =1000 , testing_limit = 400)
ross.Other_Regression(reg = 'Bridge') | No | 4 | 7 | Model_Train | train_model | 4,029,935 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 16 | A |
2,343 | # Get list of categorical variables
new= data[data.columns[~data.columns.isin(['Open Date','days','year','month'])]]
numerical_features = new.select_dtypes([np.number]).columns.tolist()
categorical_features = new.select_dtypes(exclude = [np.number,np.datetime64]).columns.tolist()
categorical_features
| No | 4 | 8 | Data_Transform | feature_engineering | 5,729,566 | 1 | A | 0 | 0 | 0 | 1 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 10 | A |
2,344 | from sklearn import preprocessing
from sklearn.model_selection import train_test_split | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
2,345 | #data = data.drop('Id', axis=1)
#test_data = test_data.drop('Id', axis=1)
y= data.revenue
x_train = data[data.columns[~data.columns.isin(['Open Date','revenue'])]] #train features to be fit in model
x_test = test_data[test_data.columns[~test_data.columns.isin(['Open Date'])]] #test features
| No | 5 | 21 | Data_Transform | prepare_x_and_y | 5,729,566 | 2 | B | 1 | 0 | 0 | 0 | 5 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 10 | B |
2,346 | from sklearn.preprocessing import LabelEncoder
# Processing the categorical columns to provide vector form of feature
class DataFrameProcess:
def __init__(self,df,col):
self.df =df
self.col=col
def dataEncoding(self):
if self.df[self.col].dtype.name == 'object' or self.df[self.col].dtype... | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 1 | 6 | 3 | 0 | 5 | 15 | ok | 10 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "Missing class docstring", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | D |
2,347 | x_train.head(5) | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,349 | from xgboost import XGBRegressor
from sklearn.ensemble import GradientBoostingRegressor
gbRegr = GradientBoostingRegressor(max_depth=3, random_state=42)
gbRegr.fit(x_train, y)
prediction_rr = gbRegr.predict(x_test)
| Yes | 4 | 7 | Model_Train | train_model | 5,729,566 | 1 | A | 1 | 0 | 0 | 0 | 5 | 5 | ok | 8 | 6 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | B |
2,350 | test_label=pd.read_csv('../input/restaurant-revenue-prediction/sampleSubmission.csv') # test target
test_label.head(10) | No | 4 | 45 | Data_Extraction | load_from_csv | 5,729,566 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/restaurant-revenue-prediction/sampleSubmission.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 11"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 10 | A |
2,351 | from sklearn.metrics import mean_squared_error
from math import sqrt
label_list=test_label['Prediction'].tolist() | Yes | 5 | 16 | Data_Transform | data_type_conversions | 5,729,566 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 10 | A |
2,352 | print('Root Mean squared error {}'.format(sqrt(mean_squared_error(label_list, prediction_rr))))
| No | 5 | 49 | Model_Evaluation | compute_test_metric | 5,729,566 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 10 | A |
2,353 | from sklearn import ensemble
params = {'n_estimators': 100, 'max_depth': 4, 'min_samples_split': 2,
'learning_rate': 0.05, 'loss': 'ls'}
GBR = ensemble.GradientBoostingRegressor(**params)
GBR.fit(x_train, y)
preds_GBR = GBR.predict(x_test)
GradientBoostingRegressor_RMSE= sqrt(mean_squared_error(label_list,... | Yes | 3 | 48 | Model_Evaluation | predict_on_test | 5,729,566 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 10 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Only MSE detected for regression. Consider adding R2 score or MAE for a more comprehensive evaluation.", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | B |
2,354 | parameters = [{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000],
'learning_rate': [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5,
0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0]
}]
from sklearn.model_sele... | Yes | 4 | 7 | Model_Train | train_model | 5,729,566 | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | ok | 5 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 16"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | A |
2,355 | final_model = XGBRegressor(n_estimators=gsearch.best_params_.get('n_estimators'),
learning_rate=gsearch.best_params_.get('learning_rate'),
n_jobs=4) | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,356 | final_model.fit(x_train, y) | No | 5 | 7 | Model_Train | train_model | 5,729,566 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | B |
2,357 | preds_test = final_model.predict(x_test) | No | 5 | 48 | Model_Evaluation | predict_on_test | 5,729,566 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 10 | A |
2,358 | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from math import sqrt
rf_model = RandomForestRegressor(random_state=1)
rf_model.fit(x_train, y)
rf_val_predictions = rf_model.predict(x_test)
RMSE = sqrt(mean_squared_error(label_list,rf_val_predictions))
print(RMSE)
| Yes | 4 | 7 | Model_Train | train_model | 5,729,566 | 1 | A | 1 | 0 | 0 | 0 | 5 | 8 | ok | 9 | 7 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Only MSE detected for regression. Consider adding R2 score or MAE for a more comprehensive evaluation.", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | B |
2,359 | submission = pd.DataFrame({
"Id": test_data["Id"],
"Prediction": rf_val_predictions
})
submission.to_csv('submission.csv',header=True, index=False)
print('done')' | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,360 | b"month2str = {1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', \\\n 7:'Jul', 8:'Aug', 9:'Sept', 10:'Oct', 11:'Nov', 12:'Dec'}\nall_data['month_str'] = all_data.Month.map(month2str)\n\ndef check(row):\n if isinstance(row['PromoInterval'],str) and row['month_str'] in row['PromoInterval']:\n return... | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,361 | tmp = all_data[all_data['part']=='train']
sns.boxplot('IsPromoMonth', 'Sales', data=tmp) | No | 5 | 33 | Visualization | distribution | 2,095,107 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,362 | all_data ['isBeforeCompetition'] = all_data.apply(lambda x: 1 if x['Year'] < x['CompetitionOpenSinceYear'] else 0, 1) | No | 5 | 14 | Data_Transform | filter | 2,095,107 | 1 | A | 0 | 1 | 1 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,363 | tmp = all_data[all_data['part']=='train']
sns.boxplot('isBeforeCompetition', 'Sales', data=tmp) | No | 5 | 33 | Visualization | distribution | 2,095,107 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,364 | fig, ax = plt.subplots(5, 1, figsize=(15,10))
for p in range (5):
i = np.random.choice(data['Store'].unique())
data[data['Store']== i ].plot('Date', 'Sales', ax=ax[p])
ax[p] .set_title("Store %d" %i)
plt.tight_layout()
plt.show()' | No | 5 | 75 | Visualization | time_series | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
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