def Create_date_list(config):
dwps = util.create_list_period(config["train_start"], config["train_end"], False)
dwp_test = util.create_list_period(config["backtest_start"], config["backtest_end"], False)
dwp, dtp = util.get_all_combination_date(dwps, 12)
return dwp_test,dwp,dtp
def setUpClass(cls) -> None:
# Generating data / params
cls.list_period = utils.create_list_period(201601, 202012)
cls.horizon = 6
cls.years_to_add = 3
cls.combination_date = utils.get_all_combination_date(
cls.list_period, cls.horizon)
# Reading test data for il
cls.raw_master_il = pd.read_csv(os.path.join(DIR_TEST_DATA,
'raw_master_il.csv'),
parse_dates=['date'])
cls.all_sales_il = pd.read_pickle(
os.path.join(DIR_TEST_DATA, 'test_all_sales_il.pkl'))
cls.forecast_il = pd.read_pickle(
os.path.join(DIR_TEST_DATA, 'test_extend_forecast_il.pkl'))
cls.pre_forecast_correction_il = pd.read_pickle(
os.path.join(DIR_TEST_DATA,
'test_apply_forecast_correction_il.pkl'))
cls.long_il = pd.read_pickle(
os.path.join(DIR_TEST_DATA, 'test_reformat_il.pkl'))
# Reading test data for dc
cls.raw_master_dc = pd.read_pickle(
os.path.join(DIR_TEST_DATA, 'raw_master_dc.pkl'))
cls.all_sales_dc = pd.read_pickle(
os.path.join(DIR_TEST_DATA, 'test_all_sales_dc.pkl'))
cls.forecast_dc = pd.read_pickle(
os.path.join(DIR_TEST_DATA, 'test_extend_forecast_dc.pkl'))
cls.pre_forecast_correction_dc = pd.read_pickle(
os.path.join(DIR_TEST_DATA,
'test_apply_forecast_correction_dc.pkl'))
cls.long_dc = pd.read_pickle(
os.path.join(DIR_TEST_DATA, 'test_reformat_dc.pkl'))
feature_importance_df_final.to_csv('./data/feature_importance_all_df.csv')
return resfinal
if __name__ == '__main__':
import src.forecaster.utilitaires as util
import src.forecaster.diagnostic as diagnostic
raw_master = pd.read_csv('./data/raw/raw_master_dc_20191126.csv')
mod = Modeldc(raw_master)
max_date_available = mod.all_sales.calendar_yearmonth.max()
filter_date = min(201909, max_date_available)
dwps = util.create_list_period(201701, filter_date, False)
dwp, dtp = util.get_all_combination_date(dwps, 12)
print("creating the main table")
table_all_features = mod.create_all_features(dwp, dtp)
# table_all_features = pd.read_csv("data/table_all_features_dc.csv")
dwp_test = util.create_list_period(201804, 201909, False)
#
# model_config = ModelConfig(
# model_name="GradientBoostingRegressor",
# model_params={
# 'standard_scaling': False,
# 'pca': 0,
# 'loss': 'huber',
# 'learning_rate': 0.01,
# 'n_estimators': 500,