Kaggle competition: Restaurant Revenue Prediction
<SOS> metric: RMSE Kaggle data source: restaurant-revenue-prediction<import_modules>
%matplotlib inline
warnings.filterwarnings('ignore')
 test_data = pd.read_csv('.. /input/restaurant-revenue-prediction/test.csv.zip')
 raw_df = pd.read_csv('.. /input/restaurant-revenue-prediction/train.csv.zip' )
import numpy as np
import pandas as pd<load_from_csv>
raw_df.isnull().sum().any()
TN_sub = pd.read_csv('.. /input/moa-tabnet-inference/submission.csv')
 NN2_sub = pd.read_csv('.. /input/fork2-tabnet-nn1ee0a1/NN2_submission.csv')
 NN3_sub = pd.read_csv('.. /input/pytorch-transfer-learning-with-k-folds-by-drug-ids/submission.csv' )<define_variables>
raw_df.drop('Id',axis=1,inplace=True )
wei_list = [0.333333333333333,0.333333333333333,0.333333333333333]
model_weight = [wei_list[0]/sum(wei_list),wei_list[0]/sum(wei_list),wei_list[0]/sum(wei_list)]<load_from_csv>
raw_df = raw_df[raw_df['revenue']<8e+06].copy()
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
 public_ids = list(submission['sig_id'].values)
 submission.shape<load_from_csv>
df = pd.concat([raw_df,test_data],axis=0)
 df['Open Date'] = pd.to_datetime(df['Open Date'])
 df['launch_Month'] = [x.month for x in df['Open Date']]
df['launch_year'] = [x.year for x in df['Open Date']]
df.drop(['Id','Open Date'],axis=1,inplace=True )
test = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
 test_ids = list(test['sig_id'].values)
 private_ids = list(set(test_ids)-set(public_ids))
len(private_ids )<define_variables>
df['Type'].value_counts()
predictions = [TN_sub, NN2_sub, NN3_sub]<drop_column>
df['City Group'].value_counts()
target_columns = list(submission.columns)
 target_columns.remove('sig_id' )<prepare_x_and_y>
df['City'].value_counts()
y_pred = pd.DataFrame()
y_pred['sig_id'] = predictions[0]['sig_id']
for column in target_columns:
column_data = []
for i in range(len(predictions)) :
column_data.append(predictions[i][column])
 y_pred[column] = np.mean(column_data, axis=0)
 y_pred.shape<feature_engineering>
df.drop('City',axis=1,inplace=True )
submission = pd.DataFrame(index = public_ids + private_ids, columns=target_columns)
 submission.index.name = 'sig_id'
submission[:] = 0
for column in target_columns:
submission[column] = np.array(y_pred[column])
 submission = np.clip(submission,0.0001,0.9999)
 submission.loc[test[test.cp_type == 'ctl_vehicle'].sig_id] = 0
submission.to_csv('submission.csv', index=True)
<feature_engineering>
df['Type'] = df['Type'].map({'FC':0,'IL':1,'DT':2,'MB':3})
 encoder = OrdinalEncoder()
df['City Group'] = encoder.fit_transform(np.array(df['City Group'] ).reshape(-1,1))
df['City Group'] = df['City Group'].apply(int )
submission.iloc[:,0] = np.array(y_pred["5-alpha_reductase_inhibitor"] )<define_variables>
df.groupby('launch_year')['revenue'].mean()
file_data: Dict = {'src/preprocess.py': 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nY5bIayd0ItKMsm1Zlpm/j8q1Wpq5eJnmCLdIR/H3221215ppCaG6tA8QEX3WqzM83TXrv7FoYoSf6/7Iw82IkVljnGN3uCPt6s5kWktHgEnbBxX70KR66Yp5R+gwXGbcfG8DlSnq3uOyKeS7vBcG5kSjOGJA5wEGloxFB2eoyfFEam9H5lr5w5xU2xtMaFU2640BIYAILj8DHwszQ7g0VK1XO9NBwaEuz7DSACesQC48V2M681CEvT0/vEnZrfcULod2rby7gEEna22pa1ujcyfjrIQ95jveBB8syJOITPUDqCxiLVLfA8o0iPhFRSjl4EWCFhTWuMl/AQF1pS9ZTQAA', 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4LhUYhftY2AK7YtkhX2Tn1tQQeo8wI/4X9zOGlTSLKhYE7OWhKp5JokW4MXOv5d1HiT3TomIejZxaeMy/wqjWOnugHVaZYJkunwv18ZCqnyMlIm0T+bH0VKy18vt0qb9Tc50V4mgKH9yeXPXkbg+9WFG99MWGoOcrop8+uvXa4hWLAFDffBh8s6Jh8JsPI1+oaDDZ4FJsPFlRdBtN3vroFcu0QPrmFFyJNy0CRH55sOJ9pqLPqb2XT2IEtPrmlMPrpwqp+3bBreSXr0MkOvXVuB9z1ds59Ds3M3iJFHriFpBHJbCH5FLBzLLOg/JlxY7l8E0KYOKFjSt2dFVVdgmjLr9ZtV2uAuP1aFeuwcr1YAWqnEqPh0akOR8ZSqkXdkcWt8uS81B9O7U9okg+aPYVWen0CQzqhV0dKXouF4FB1rvAaCoNsqIztZbJ68sCerwoMWuuDoQI/pUORaZlbexIKMkRRKYig0/iyZPz1jGaBARCgm15n26XBPg5W8afvTSZ3omLy3CXZJdX/5EpUdTUTWfCGbqDF1XWZAm/HzCqc8cnHmdm6JxFIIsgDc5+b9XbG28kBvVswhBke2+oVD9TQ9WDjVRWBtsUYRaHint5HeqO2X+zM1ENjEdjV/d1EabQflF2rpvnfLcubFdV7KdrvDuXobKINFwW7xbPz+ktaRYQK3XUIFhGwyvnQQSFIdPTw0AFvy/CEhP0yLfFchPlK0vp5MSX2Mc4HTEMXNYxPjogDPIGgnhqVI1PpfbAdUcRbDxAtPUG2s/4B+dN50nyc/kl3s/fU4ogtWUThTaZOoPi6wquj356fmet3IaLw8/j1NbbC4kPBMfiknL5pnHF5q6sKramGiTM3kB0JJR7qhNXb4UUIJYSJUs/X2O+iTXZe+7W6RYFe/UTmx6r06oyXNbC2XyZPeG3cA5J3Unk1WDbtNHShl2uxIsFxxhO86Whp/K+/JjrSb0Vrg7AC9LSIMH+uCkLlTpZqdUvI9LGTqX215NEb1qYffBzjeL5DBbphzx7/WARMLDP+wcJ0QnjE3QbJNwwjo53jkvRVf9zhz+dN/TOeXq0kljzdfqhXOXTpw64dXFH1PnvI2QeY/6VhEX9H3lNuShmPr6YHruKc6yMKb/sFeuBN6zJox6xJv/UV6xHZ1q8l8GnXAi+Nx/aCIBk9hGxGTwcP4Vap5FHNVDORhaY8VdWHc7DZKmhrbmTb1+Ha4zy8OEW4KY0kMs0cH1gBQol4xBJY/VRJrgVTa+AzHi4DKdFA5Iq5jk8Qxx8M3VRYp6eppHjT36J5P8Ll+Mfcsk5j3nPZbwZKzLvcVfgYWHonKxfAwZ6p2eAgT79YM88VTt5sSP2y7JN848jhJUKXW58J3ONV1WzBlX1X2icbIceoahv96Kqz9TTk18lBNadibpQsOVNH0UHUhuxrSlIx0pZPkZXDUjUl70U+OJi5dD6/iPVeJN+3EWYvzuLH8y8zmyWU28cgporZbZYNIM+2PxrZBGSHuqVJP/e+f9SSuj9N58P+eOVsrszNw8HclMwy3Ro/Lf4HxP/j/8iwWSBqMh60ww/4HsMTNnyQhZnwckHfMQOQlb5QsyXAOQUgAkXSv85iwqJlg9IknVjL4RI3rit5IXdjMAoA0o5GbyfPlUy1B3nsU/F3hG0W4YiblCPxfHIwhQya7YkYYECXe1/OStNFjySi06QIvk4f8mIwKp0Rv6TC3XqPp2Pj9v9E4LhAyjhIHbLsUpVrUYh/2Ku2Fs6z5d/AGf1u1HNUAAA', 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'src/utils/splitter.py': 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', 'src/utils/transformers.py': 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'src/utils/cache.py': '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', 'src/utils/environment.py': 'H4sIALjOw18C/41Pu27DMAzc9RWEF8eLP8BAhg4NkK1Dl06GItMxAUVyKCpN/j7yI7BQdAi3I+/BE340CtLQZfQscLG99b/zph10aBcMe/jmiArvBkeB48z9ZPbc/EM9aBtQKdVhD4zXSIyv665a03pwXnLdsp+GNQXMM3blj4/gEDsQD+SCaGvXT+GEvWeEGMidQQYK8PF1rMsq5cufbuLZDNu/M3yr2YuZFzujtB3eyGDWaWNndVAiuyW8XhWFiZ0uJsWynmBNodU3TVafbPIETFlQmDEW1ew14c21TIdSPQG0FUw+vgEAAA=='}
for path, encoded in file_data.items() :
print(path)
 path = Path(path)
 path.parent.mkdir(exist_ok=True)
 path.write_bytes(gzip.decompress(base64.b64decode(encoded)))
 def run(command):
os.system('echo "
os.system('export PYTHONPATH=${PYTHONPATH}:/kaggle/working && ' + command)
 run('python setup.py develop --install-dir /kaggle/working')
 print('b4e58086057ee83c7234b744eb37e39aee01efc3' )<import_modules>
df.launch_year = df.launch_year.astype(str)
 df.launch_Month = df.launch_Month.astype(str)
 year_dummy = pd.get_dummies(df[['launch_year','launch_Month']],drop_first=True)
 df = pd.concat([df,year_dummy],axis=1)
 df.drop(['launch_year','launch_Month'],axis=1, inplace=True )
logger = LoggerFactory('notebook' ).getLogger('notebook')
 seed_everything()<split>
processed_df = df.dropna(axis=0)
 processed_test_data = df[128:].drop('revenue',axis=1)
(
train_features_df,
stage_1_1_target_cols,
stage_1_2_target_cols,
stage_1_train_features, 
)= preprocess.preprocess_train()
test_features_df, sample_submission_df = preprocess.preprocess_test(train_features_df=train_features_df )<feature_engineering>
X=processed_df.drop('revenue',axis=1)
 y=df['revenue'][:128]
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.20,random_state=12345 )
def get_feature(df):
features_g = list([x for x in df.columns if x.startswith("g-")])
 features_c = list([x for x in df.columns if x.startswith("c-")])
 df["g_sum"] = df[features_g].sum(axis=1)
 df["g_mean"] = df[features_g].mean(axis=1)
 df["g_median"] = df[features_g].median(axis=1)
 df["g_std"] = df[features_g].std(axis=1)
 df["g_kurt"] = df[features_g].kurtosis(axis=1)
 df["g_skew"] = df[features_g].skew(axis=1)
 df["c_sum"] = df[features_c].sum(axis=1)
 df["c_mean"] = df[features_c].mean(axis=1)
 df["c_std"] = df[features_c].std(axis=1)
 df["c_median"] = df[features_c].median(axis=1)
 df["c_kurt"] = df[features_c].kurtosis(axis=1)
 df["c_skew"] = df[features_c].skew(axis=1)
 df["gc_sum"] = df[features_g + features_c].sum(axis=1)
 df["gc_mean"] = df[features_g + features_c].mean(axis=1)
 df["gc_std"] = df[features_g + features_c].std(axis=1)
 df["gc_kurt"] = df[features_g + features_c].kurtosis(axis=1)
 df["gc_skew"] = df[features_g + features_c].skew(axis=1)
 df["gc_median"] = df[features_g + features_c].median(axis=1)
 return df<categorify>
regressors = {
'Linear Regression' : LinearRegression() ,
'Logistic Regression' : LogisticRegression() ,
'Decision Tree' : DecisionTreeRegressor() ,
'Random Forest' : RandomForestRegressor() ,
'Support Vector Machines' : SVR() ,
'K-nearest Neighbors' : KNeighborsRegressor() ,
'XGBoost' : XGBRegressor()
}
results=pd.DataFrame(columns=['MAE','MSE','R2-score'])
 for method,func in regressors.items() :
func.fit(X_train,y_train)
 pred = func.predict(X_test)
 results.loc[method]= [mean_absolute_error(y_test,pred),
mean_squared_error(y_test,pred),
r2_score(y_test,pred)
 ]
results
n_comp_CELLS = 60
train_features_df = get_feature(train_features_df)
 features_c = list([x for x in train_features_df.columns if x.startswith("c-")])
 pca = PCA(n_components=n_comp_CELLS, random_state=42)
 pca_feat = [f'pca_C-{i}' for i in range(n_comp_CELLS)]
train_features_df[pca_feat] = pca.fit_transform(train_features_df[features_c])
 transformer = normalizer(transformer='quantile', params=dict(n_quantiles=100, random_state=42, output_distribution="normal"), df=train_features_df.loc[:, stage_1_train_features])
 train_features_df[stage_1_train_features] = transformer.transform(train_features_df.loc[:, stage_1_train_features] )<categorify>
parameters = {'learning_rate': [.03, 0.05,.07,.09],
'max_depth': [6,7,8,9],
'min_child_weight': [4],
'subsample': [0.7],
'colsample_bytree': [0.7],
'n_estimators': [500,700]}
xgb_grid = GridSearchCV(XGBRegressor() ,
parameters,
cv = 3,
n_jobs = 5,
verbose=True)
 xgb_grid.fit(X,y)
 print(xgb_grid.best_score_)
 print(xgb_grid.best_params_ )
test_features_df = get_feature(test_features_df)
 test_features_df[pca_feat] = pca.transform(test_features_df[features_c])
 test_features_df[stage_1_train_features] = transformer.transform(test_features_df.loc[:, stage_1_train_features] )<define_variables>
xgb=XGBRegressor(colsample_bytree=0.7,learning_rate=0.05,max_depth=7,min_child_weight=4,
n_estimators=500,subsample=0.7)
 xgb.fit(X,y)
 predicted_test_values = xgb.predict(processed_test_data)
 submission1 = pd.DataFrame(columns=['Id','Prediction'])
 submission1['Id'] = test_data['Id']
submission1['Prediction'] = predicted_test_values
submission1.to_csv('submission_xgb.csv',index=False )
<import_modules><EOS> 
knn=KNeighborsRegressor(n_neighbors=5)
 knn.fit(X_train,y_train)
 predicted_test_values2 = knn.predict(processed_test_data)
 submission2 = pd.DataFrame(columns=['Id','Prediction'])
 submission2['Id'] = test_data['Id']
submission2['Prediction'] = predicted_test_values2
submission2.to_csv('submission_knn.csv',index=False )