function/train_modeling.py

import pandas as pd
import numpy as np
import gradio as gr
import datetime
from dateutil.relativedelta import relativedelta
import datarobot as dr
from function import get_fish_qty, get_estat, dr_prediction_deployment

import yaml
with open('config.yaml') as file:
    config = yaml.safe_load(file.read())

def create_train_data():
    # ターゲットを抽出
    household_survey = get_estat.get_household_survey()
    expence_df = pd.DataFrame({'年月':household_survey['時間軸（月次）'].unique()})
    cate='3.1 電気代'
    temp_df = household_survey.loc[household_survey['品目分類（2020年改定）'] == cate]
    unit = temp_df['unit'].unique()[0]
    temp_df = temp_df.rename(columns={'value':f'{cate}_({unit})'})
    expence_df = pd.merge(expence_df,
                          temp_df[['時間軸（月次）', f'{cate}_({unit})']].rename(columns={'時間軸（月次）':'年月'}),
                          on='年月',
                          how='left')
    expence_df = expence_df.rename(columns={'3.1 電気代_(円)':'電気代'})
    expence_df['年月'] = pd.to_datetime(expence_df['年月'], format='%Y年%m月').astype(str).apply(lambda x:''.join(x.split('-'))[:6]).astype(int)
    
    # 原油価格を抽出し作成
    oil_price_df = pd.read_excel(config['oil_price_url'], header=5)
    oil_price_df = oil_price_df.rename(columns={oil_price_df.columns[0]:'年'})
    oil_price_df['年'] = oil_price_df['年'].interpolate(method='ffill')
    oil_price_df['年月'] = oil_price_df['年'] + oil_price_df['月'].astype(str) + '月'
    oil_price_df['年月'] = pd.to_datetime(oil_price_df['年月'], format='%Y年%m月').astype(str).apply(lambda x:''.join(x.split('-'))[:6]).astype(int)
    
    # 燃料調達価格のデータを作成
    fuel_procurement_cost_df = pd.read_excel(config['fuel_procurement_cost_url'], header=4)
    fuel_procurement_cost_df = fuel_procurement_cost_df.iloc[:, 3:]
    for i in fuel_procurement_cost_df.columns:
        if '\n' in i:
            fuel_procurement_cost_df = fuel_procurement_cost_df.rename(columns={i:i.replace('\n', '')})

    fuel_procurement_cost_df['燃料費調整単価適用期間'] = fuel_procurement_cost_df['燃料費調整単価適用期間'].interpolate(method='ffill')
    fuel_procurement_cost_df['燃料費調整単価適用期間'] = pd.to_datetime(fuel_procurement_cost_df['燃料費調整単価適用期間'],
                                                             format='%Y年\n%m月').astype(str).apply(lambda x:''.join(x.split('-'))[:6]).astype(int)
    for kind in fuel_procurement_cost_df['種別'].unique():
        temp_df = fuel_procurement_cost_df.loc[fuel_procurement_cost_df['種別']==kind].drop('種別', axis=1)
        temp_df = temp_df.rename(columns={temp_df.columns[0]:'年月'})
        for i in temp_df.columns:
            if i != '年月':
                temp_df = temp_df.rename(columns={i:f'{i}_{kind}_lag1'})
                temp_df[f'{i}_{kind}_lag1'] = temp_df[f'{i}_{kind}_lag1'].shift(1)
        expence_df = pd.merge(expence_df,
                              temp_df,
                              on='年月',
                              how='left')
        
    # 各データを結合
    oil_price_df[['ブレント_lag3', 'ドバイ_lag3', 'WTI_lag3', 'OPECバスケット_lag3']] = oil_price_df[['ブレント', 'ドバイ', 'WTI', 'OPECバスケット']].shift(3)
    expence_df = pd.merge(expence_df,
                          oil_price_df[['ブレント_lag3', 'ドバイ_lag3', 'WTI_lag3', 'OPECバスケット_lag3', '年月']],
                          on='年月',
                          how='left')
    
    # 魚の卸売りデータを読み込み
    last_time_fish_arch = pd.read_csv('data/fish_sell_ach.csv')
    start_date = pd.to_datetime(str(int(last_time_fish_arch['date'].max())))
    today = datetime.datetime.now()
    end_date = pd.to_datetime(today + relativedelta(days=1))
    use_fish_list = config['use_fish_list']
    temp_sell_ach = get_fish_qty.get_fish_price_data(start_date, end_date, use_fish_list)
    temp_sell_ach['date'] = temp_sell_ach['date'].astype(int)
    sell_ach = pd.concat([last_time_fish_arch,
                                              temp_sell_ach.loc[~temp_sell_ach['date'].isin(last_time_fish_arch['date'].unique())]])
    sell_ach.to_csv('data/fish_sell_ach.csv', index=False)
    
    # trainデータの作成
    sell_ach['target_date'] = sell_ach['date'].apply(lambda x:int((pd.to_datetime(str(x))+relativedelta(months=1)).strftime('%Y%m%d')))
    sell_ach['年月'] = sell_ach['target_date'].astype(str).str[:6].astype(int)
    
    col_list=['するめいか_卸売数量計(kg)',
                 'いわし_卸売数量計(kg)',
                 'ぶり・わらさ_卸売数量計(kg)',
                 '冷さけ_卸売数量計(kg)', 
                 '塩さけ_卸売数量計(kg)',
                 'さけます類_卸売数量計(kg)',
                 '全卸売数量計(kg)']
    
    for shift_i in [7, 14, 21, 28]:
        change_col_list = [f'{i}_lag{shift_i}' for i in col_list]
        sell_ach[change_col_list] = sell_ach[col_list].shift(shift_i)
    
    sell_ach = sell_ach.rename(columns={'date':'forecast_point'})
    train_df = pd.merge(expence_df, 
                        sell_ach,
                        on='年月')
    train_df.to_csv('data/train.csv', index=False)
    
    return train_df


def modeling():
    train_df = create_train_data()
    # モデリングに必要な各設定値
    ## データロボットとの接続設定
    token = 'NjQwMDVmNGI0ZDQzZDFhYzI2YThmZDJiOnVZejljTXFNTXNoUnlKMStoUFhXSFdYMEZRck9lY3dobnEvRFZ1aVBHbVE9'
    ### デモ環境これっぽい
    endpoint = 'https://app.datarobot.com/api/v2' 

    ## プロジェクト名
    project_name = f'{datetime.datetime.now().strftime("%Y%m%d")}_ESTYLEU_電気代予測_再学習'

    ## 各種設定
    ### 特徴量設定
    target = '電気代'
    feature_timeline = 'target_date'    #時系列
    not_use_feature = ['年月', 'forecast_point']
    # 最適化指標
    metric = 'RMSE'
    ### ギャップ
    gap='P0Y'       #　これで0？要確認
    ### バックテストの数
    number_of_backtests = 1
    end_date = int(train_df[feature_timeline].max())
    ### 日付
    holdout_end_date=pd.to_datetime(str(end_date))
    holdout_start_date=holdout_end_date - relativedelta(years=1)
    backtest_end_date = holdout_start_date - relativedelta(days=1)
    backtest_start_date = backtest_end_date - relativedelta(years=1)
    train_end_date = backtest_start_date - relativedelta(days=1)
    train_start_date = pd.to_datetime(str(int(train_df[feature_timeline].min())))

    ### モデリングモード
    mode = dr.AUTOPILOT_MODE.QUICK
#     mode = dr.AUTOPILOT_MODE.FULL_AUTO
    dr.Client(
                    endpoint=endpoint,
                    token=token
                )

    # バックテスト設定
    backtests_setting = [dr.BacktestSpecification(
                                                                            index=0,
                                                                            primary_training_start_date=train_start_date, 
                                                                            primary_training_end_date=train_end_date,
                                                                            validation_start_date=backtest_start_date, 
                                                                            validation_end_date=backtest_end_date
                                                                        )]

    spec = dr.DatetimePartitioningSpecification(
                                                                        feature_timeline,
                                                                        use_time_series=False,
                                                                        disable_holdout=False,
                                                                        holdout_start_date=holdout_start_date,
                                                                        holdout_end_date=holdout_end_date,
                                                                        gap_duration=gap,
                                                                        number_of_backtests=number_of_backtests,
                                                                        backtests=backtests_setting,
                                                                    )

    use_feature_list = train_df.columns.to_list()

    print('now creating project')
    project = dr.Project.create(
                                                train_df, 
                                                project_name=project_name
                                            )

    raw = [feat_list for feat_list in project.get_featurelists() if feat_list.name == 'Informative Features'][0]
    raw_features = [feat for feat in raw.features if f'{feature_timeline} ' in feat]

    for i in not_use_feature:
        if i in use_feature_list:
            use_feature_list.remove(i)

    use_feature_list = use_feature_list.extend(raw_features)
    print("start modeling")
    project.analyze_and_model(
                                                target = target,
                                                mode = mode,
                                                partitioning_method=spec,
                                                max_wait=3000,
                                                worker_count=-1,
                                                featurelist_id = project.create_featurelist('モデリング', use_feature_list).id
                                            )
    project.wait_for_autopilot()
    project.unlock_holdout()
    
    model_df = pd.DataFrame(
        [[model.id,
          model.model_type,
          model.metrics['RMSE']['validation'],
          model.metrics['RMSE']['backtesting'],
          model.metrics['RMSE']['holdout'],
          model] for model in project.get_datetime_models() if model.model_type != 'Baseline Predictions Using Most Recent Value'],
        columns=['ID', 'モデル名', 'バックテスト1', '全てのバックテスト', 'holdout', 'model'])
    model_df = model_df.sort_values('holdout').reset_index(drop=True)
    
    model = model_df['model'][0]
    
    model_management_df = pd.DataFrame()
        
    temp_model_management_df = pd.DataFrame({
                                                                           '作成日':[int(datetime.datetime.now().strftime('%Y%m%d'))],
                                                                           '作成時間':[int(datetime.datetime.now().strftime('%H%M%S'))],
                                                                           'project_url':[project.get_uri()],
                                                                           'model_url':[model.get_uri()],
                                                                           'model_type':[model.model_type]
                                                                         })
    
    model_management_df = pd.concat([model_management_df,
                                                            temp_model_management_df])
    model_management_df.to_csv('data/temp_model_management.csv')
    
    deployment = dr.Deployment.get(deployment_id='640d791796a6a52d92c368a0')
    
    deployment.replace_model(model.id, dr.enums.MODEL_REPLACEMENT_REASON.SCHEDULED_REFRESH)
    
    return model_management_df