# coding: utf-8
import logging
import unittest
import pandas as pd
from cfg.paths import LOG_FILENAME
from src.tradeflow.TradeflowDC import TradeflowDC
from src.tradeflow.TradeflowIL import TradeflowIL
from src.utils.logging import setup_loggers
logger = logging.getLogger(__name__)
setup_loggers('test_tradeflow', log_filepath=LOG_FILENAME, level=logging.INFO)
class TestTradeflow(unittest.TestCase):
def test_offtake_setter(self):
offtake = pd.Series([10, 8, 9, 10, 10, 11, 9, 9, 8, 9, 14, 10])
ic_r = pd.Series([0.5] * offtake.size)
ic_s = pd.Series([1.0] * offtake.size)
tradeflow = TradeflowDC()
tradeflow.offtake = offtake.copy()
tradeflow.ic_r = ic_r.copy()
tradeflow.ic_s = ic_s.copy()
tradeflow.update_from_offtake()
new_offtake = pd.Series([10, 8, 9, 10, 11, 11, 9, 9, 8, 9, 14, 10])
tradeflow.offtake = new_offtake.copy()
tradeflow.update_from_offtake()
RUN_DATETIME
from cfg.paths import LOG_FILENAME
from run import get_cycle_date
from src.data_wrangling import F_DI_TRADEFLOW, F_IL_OFFTAKE, F_IL_SELLIN, F_IL_OFFTAKE_CHANNEL, F_IL_MAP_SELLIN_EIB
from src.data_wrangling.Data import Data
from src.data_wrangling.RawMasterIL import RawMasterIL
from src.export_results.ExportFeatureImportance import ExportFeatureImportance
from src.forecaster.ForecasterIL import ForecasterIL
from src.postprocessing.postprocessing import PostProcessing
from src.scenario import F_RESULTS, F_DATABASE, F_TABLE, F_CONNECT, V_IL, F_FEATURE_IMPORTANCE
from src.scenario.Scenario import Scenario
from src.utils.logging import setup_loggers
from src.utils.misc import run_cmd
logger = logging.getLogger(__name__)
setup_loggers('demand_forecast_il', log_filepath=LOG_FILENAME)
DATA_FROM_CACHE = False
FORECASTS_FROM_CACHE = False
POSTPROC_FROM_CACHE = False
SCENARIO_FROM_CACHE = False
SAVE_TO_FS = True
SAVE_TO_IMPALA = True
# PATH_CSV_RESULTS = DIR_DATA # todo Zhaoxia: set a path to store output as CSV
def run_il(date_start: int, horizon: int) -> None:
""" Defines the flow to calculate IL forecasts based on the data available in Impala.
:return:
# coding: utf-8
import logging
import os
import unittest
import pandas as pd
import yaml
from cfg.paths import CFG_ADDL_GRAN, DIR_CACHE
from cfg.paths import LOG_FILENAME
from src.postprocessing.postprocessing import PostProcessing
from src.utils.logging import setup_loggers
logger = logging.getLogger(__name__)
setup_loggers('test_postprocessing', log_filepath=LOG_FILENAME, level=logging.INFO)
class AddGranTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
with open(CFG_ADDL_GRAN) as f:
cfg = yaml.load(f)
cls.df_hist_di = pd.read_csv(cfg['di']['history'], sep=';', low_memory=False)
cls.df_hist_il = pd.read_csv(cfg['il']['history'], sep=';', low_memory=False)
cls.df_hist_dc = pd.read_csv(cfg['dc']['offtake_history'], sep=';', low_memory=False)
cls.df_hist_il_sellin = pd.read_csv(cfg['il']['sellin_history'], sep=';', low_memory=False)
cls.df_hist_eib = pd.read_csv(cfg['il']['eib_history'], sep=';', low_memory=False)
cls.df_fcst_il_all = pd.read_csv(cfg['il']['forecast'])
cls.df_fcst_dc = pd.read_csv(cfg['dc']['forecast'])
import logging
import os
import unittest
from datetime import date
import numpy as np
import pandas as pd
from dateutil.relativedelta import relativedelta
from cfg.paths import LOG_FILENAME, DIR_TEST_DATA
from src.deagg.Deagg import Deagg
from src.scenario import *
from src.utils.logging import setup_loggers
logger = logging.getLogger(__name__)
setup_loggers('test_deagg', log_filepath=LOG_FILENAME, level=logging.INFO)
today = date.today()
class TestDeagg(unittest.TestCase):
def test_deagg(self):
dummy_forecasts = pd.DataFrame({
F_DN_MEA_DAT:
[today] * 25 + [today + relativedelta(months=1)] * 25 +
[today + relativedelta(months=2)] * 25 +
[today + relativedelta(months=3)] * 25,
F_DN_LV2_UMB_BRD_COD: ['DC'] * 100,
F_DN_LV3_PDT_BRD_COD: ['AP'] * 50 + ['NC'] * 50,
F_DN_LV5_PDT_SFM_COD:
['1'] * 25 + ['2'] * 25 + ['1'] * 25 + ['2'] * 25,
import pandas as pd
import yaml
from cfg.paths import DIR_CFG, DIR_CACHE
from cfg.paths import LOG_FILENAME
from src.data_wrangling.RawMasterDC import SELECTED_SKUS as SELECTED_SKU_DC
from src.data_wrangling.RawMasterIL import EXCLUDED_SKUS as EXCLUDED_SKUS_IL
from src.scenario import *
from src.scenario import F_DN_OFT_VAL, F_DN_MEA_DAT, F_CONNECT, F_RESULTS, F_DATABASE, F_TABLE, \
F_DN_FRC_USR_NAM_DSC, F_DN_FRC_CRE_DAT, F_DN_FRC_MDF_DAT, F_DN_DATE_FMT
from src.scenario.Scenario import Scenario
from src.utils.impala_utils import ImpalaUtils
from src.utils.logging import setup_loggers
logger = logging.getLogger(__name__)
setup_loggers('test_data_pipeline', log_filepath=LOG_FILENAME, level=logging.INFO)
COMP_SELLOUT = 'comp_sellout'
COMP_SELLIN = 'comp_sellin'
COMP_OFFTAKE = 'comp_offtake'
class TestDataPipeline(unittest.TestCase):
# @unittest.skip('Skipped')
def test_check_il_raw_to_postproc(self):
# load data obtained from run.demand_forecast_il
data_il_raw_master: pd.DataFrame = pd.read_pickle(os.path.join(DIR_CACHE, 'raw_master_il.pkl'))
data_il_output_postproc: pd.DataFrame = pd.read_pickle(os.path.join(DIR_CACHE, 'il_actual_split.pkl'))
first_forecast_month = data_il_output_postproc[F_AF_DATE].max()