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] try: model_management_df = read_csv('data/model_management.csv') except: 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/model_management.csv') deployment = dr.Deployment.get(deployment_id='640d791796a6a52d92c368a0') deployment.replace_model(model.id, dr.enums.MODEL_REPLACEMENT_REASON.SCHEDULED_REFRESH)