Upload train_modeling.py

function/train_modeling.py

@@ -0,0 +1,185 @@
+import pandas as pd
+import numpy as np
+import gradio as gr
+import datetime
+from dateutil.relativedelta import relativedelta
+from func 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(last_time_fish_arch['date'].max()))
+    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')
+    
+    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 = train_df[target].max()
+        ### 日付
+        holdout_end_date=pd.to_datetime(str(end_date))
+        holdout_start_date=holdout_end_date - relativedelta(year=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(train_df[target].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 = 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
+                                                )
+        print(project.get_leaderboard_ui_permalink())
+        project.wait_for_autopilot()
+        
+