app.py

import pandas as pd
import numpy as np
import gradio as gr
import datetime
import calendar
import matplotlib.pyplot as plt
import japanize_matplotlib
import matplotlib.dates as mdates
from dateutil.relativedelta import relativedelta
import datetime
import datarobot as dr
from function import get_fish_qty, get_estat, dr_prediction_deployment, prediction_func, train_modeling

import yaml
with open('config.yaml') as file:
    config = yaml.safe_load(file.read())
    
def retrain():
    model_management_df = train_modeling.modeling()
    
    model = dr.Model.get(project = dr.Project.get(model_management_df.iloc[0, :]['model_url'].split('/')[4]),
                                         model_id = model_management_df.iloc[0, :]['model_url'].split('/')[-1])
    feature_impact = pd.DataFrame(model.get_or_request_feature_impact())
    feature_impact = feature_impact.sort_values('impactNormalized', ascending=False).reset_index(drop=True)
    feature_impact = feature_impact.iloc[:20, :]
    for i in range(len(feature_impact)):
                feature_impact['featureName'][i] = str(i+1).zfill(2) + '_' + feature_impact['featureName'][i]
            
    return model_management_df.iloc[0, :]['model_type'], model.metrics['RMSE']['holdout'], feature_impact
    

def get_prediction_result():
    today = datetime.datetime.now()
    prediction_month = (today+relativedelta(months=1)).strftime('%Y%m')
    month_days = month_days = [pd.to_datetime(prediction_month + str(i+1).zfill(2)) for i in range(calendar.monthrange((today+relativedelta(months=1)).year, (today+relativedelta(months=1)).month)[1])]
    dfc = pd.DataFrame({'target_date':month_days})
    df = prediction_func.prediction_to_dr(config['oil_price_url'], config['fuel_procurement_cost_url'])
    df = df.loc[df['target_date'].astype(str).str[:6]==prediction_month]
    df['target_date'] = pd.to_datetime(df['target_date'].astype(str))
    df['forecast_point'] = pd.to_datetime(df['forecast_point'].astype(str))
    df = pd.merge(dfc,
                  df,
                  on='target_date',
                  how='left')
    df.loc[df['forecast_point'].isnull(), 'forecast_point'] = df['target_date'].apply(lambda x:x-relativedelta(months=1))
    df = df.loc[~((df['target_date']<(today+relativedelta(months=1)))&(df['電気代'].isnull()))]
    df = df.rename(columns={'電気代':'電気代_予測'})
    return df[['forecast_point', 'target_date', '電気代_予測']]

def plot_prediction_result():
    update = gr.LinePlot.update(
        value=get_prediction_result(),
        x="target_date",
        y="電気代_予測",
        title="昨日までの魚の卸売り量から予測された、来月の2人世帯の平均電気料金の推移",
        width=500,
        height=300,
    )
    return update
    
def get_model_infomation():
    token = 'NjQwMDVmNGI0ZDQzZDFhYzI2YThmZDJiOnVZejljTXFNTXNoUnlKMStoUFhXSFdYMEZRck9lY3dobnEvRFZ1aVBHbVE9'
    endpoint = 'https://app.datarobot.com/api/v2' 
    dr.Client(
                        endpoint=endpoint,
                        token=token
                    )
    project = dr.Project.get([i for i in dr.Project.list() if '電気代予測' in str(i)][0].id)
    
    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_info = {}
    model_info['RMSE'] = model.metrics['RMSE']['holdout']
    model_info['model_type'] = model.model_type
    model_info['model_type'] = model.model_type

    feature_impact = pd.DataFrame(model.get_or_request_feature_impact())
    feature_impact = feature_impact.sort_values('impactNormalized', ascending=False).reset_index(drop=True)
    feature_impact = feature_impact.iloc[:20, :]
    
    
    return model_info, feature_impact

def get_featuredrift():
    deployment = dr.Deployment.get(deployment_id='640d791796a6a52d92c368a0')
    target_drift = dr.models.TargetDrift.get(deployment.id)
    feature_drift_list = dr.models.FeatureDrift.list(deployment.id)
    drift_df = pd.DataFrame(
                                            {
                                            'feature_name':[target_drift.target_name], 
                                            'drift_score':[target_drift.drift_score], 
                                            'feature_impact':[1]
                                            }
                                        )
    drift_df = pd.concat([
                                    drift_df,
                                    pd.DataFrame(
                                                        [[
                                                            feature_drift.name,
                                                            feature_drift.drift_score,
                                                            feature_drift.feature_impact
                                                         ] for feature_drift in feature_drift_list
                                    ],
                                        columns=[ 'feature_name', 'drift_score', 'feature_impact']
                                    )
    ])
    start_point = (target_drift.period['start']+relativedelta(hours=9)).strftime("%Y / %m / %d %H:%M:%S")
    end_point = (target_drift.period['end']+relativedelta(hours=9)).strftime("%Y / %m / %d %H:%M:%S")
#         drift_df.loc[(drift_df['drift_score']>drift_threshold)&(drift_df['feature_impact']>impact_threshold), 'alert'] = '重要性の高く、大きなドリフト'
#         drift_df.loc[(drift_df['drift_score']>drift_threshold)&(drift_df['feature_impact']<=impact_threshold), 'alert'] = '重要性低いが、大きなドリフト'
#         drift_df.loc[(drift_df['drift_score']<=drift_threshold), 'alert'] = '正常'

    return drift_df, start_point, end_point

with gr.Blocks() as electoric_ploting:
    gr.Markdown(
                            """
                            # その日の魚の卸売り量から、来月の家計データ月別支出の電気代を予測するAI
                            使用データ  
                             * 東京卸売市場日報
                             * 家計調査の月別支出
                             * 原油価格データ
                             * 燃料調達価格データ  
                            why  
                            電気代のtrendは原油価格などが大きく影響するが、細かい変化は気候に影響し、気候はある程度海水温に関連性があると考えられる。
                            また、魚の卸売量は水揚げ量に関係し、水揚げ量は海水温に関係するという考えからモデルを作成。
                             """
                        )
    with gr.Tab("予測結果"):
        with gr.Row():
            with gr.Column():
                plot = gr.LinePlot(show_label=False)
    #             plot = gr.Plot(label="昨日までの魚の卸売り量から予測された、来月の2人世帯の平均電気料金の推移")
            with gr.Column():
                df = get_prediction_result()
                gr.Textbox(df['電気代_予測'].max(),
                                label='現在までの予測値の最大値')
                gr.Textbox(df['電気代_予測'].min(),
                                label='現在までの予測値の最小値')
                gr.Textbox(df['電気代_予測'].mean(),
                                label='現在までの予測値の平均値')
                gr.Textbox(df['電気代_予測'].median(),
                                label='現在までの予測値の中央値')
        with gr.Row():
            gr.DataFrame(get_prediction_result)
            
            
    with gr.Tab("モデル情報"):
        gr.Markdown(
                            """
                            注意：  
                            再学習後はモデルのデプロイが自動で行われます。  
                            huggingfaceの使用上csvを上書きできないため。
                             """
                        )
        retrain_btn= gr.Button(value="再学習")
        with gr.Row():
            with gr.Column():
                model_info, feature_impact_df = get_model_infomation()
                gr.Textbox(model_info['model_type'], label='現在のモデル')
                
            with gr.Column():
                output_model_type = gr.Textbox(label='再学習後のモデル')
                
        with gr.Row():
            with gr.Column():
                gr.Textbox(model_info['RMSE'],label=f'Holdout RMSE精度')
            with gr.Column():
                output_acc = gr.Textbox(label='再学習後のHoldout RMSE精度')

        with gr.Row():
            with gr.Column():
                for i in range(len(feature_impact_df)):
                    feature_impact_df['featureName'][i] = str(i+1).zfill(2) + '_' + feature_impact_df['featureName'][i]
                gr.BarPlot(value = feature_impact_df,
                                title = '特徴量インパクト上位20',
                                x = 'featureName',
                                y = 'impactNormalized',
                                tooltip=['impactNormalized'],
                                x_title = '特徴量名',
                                y_title = '特徴量インパクト_相対値',
                                vertical=False,
                                y_lim=[0, 1.2],
                                width=400,
                                height=300)
            with gr.Column():
                 output_plot = gr.BarPlot(title = '再学習後特徴量インパクト上位20',
                                                    x = 'featureName',
                                                    y = 'impactNormalized',
                                                    tooltip=['impactNormalized'],
                                                    x_title = '特徴量名',
                                                    y_title = '特徴量インパクト_相対値',
                                                    vertical=False,
                                                    y_lim=[0, 1.2],
                                                    width=400,
                                                    height=300)
    with gr.Tab("データドリフト情報"):
        result = get_featuredrift()
        with gr.Row():
            gr.Markdown(
                        """
                        こちらの図はデータドリフトと特徴量の有用性を表した図になっています。  
                        味方は以下の通り  
                         * ドリフトスコア：予測データに含まれるデータが、どれぐらい過去のデータに比べてずれが発生しているかを表しており、上に行けば行くほどズレが大きい
                         * 特徴量の有用性：ターゲットの有用性を1とした時に、どれぐらいそれぞれの特徴量の有用性が高いかを表したもので、右に行くほど有用性が高い
                         """
                    )
        with gr.Row():
            drift_df = result[0]
            start_point = result[1]
            end_point = result[2]
            gr.Textbox(f"{start_point}〜{end_point}",label=f'データドリフト確認期間')
        with gr.Row():
            if len(drift_df["drift_score"].unique())!=1:
                gr.ScatterPlot(
                                        drift_df,
                                        x="feature_impact",
                                        y="drift_score",
                                        title="データドリフトとデータの有用性",
                                        color_legend_title="Species",
                                        x_title="特徴量の有用性",
                                        y_title="ドリフトスコア",
                                        x_lim = [-0.1, drift_df["feature_impact"].max()*1.4],
                                        y_lim = [-0.1, drift_df["drift_score"].max()*1.4],
                                        tooltip=["feature_name", "feature_impact", "drift_score"],
                                        caption="",
                                        height=500,
                                        width=500
                                    )
            else:
                gr.Markdown(
                    """
                    モデルの入れ替え後に予測が実行されていないためdriftは表示できません。
                     """
                )

    retrain_btn.click(retrain, inputs=None, outputs = [output_model_type, output_acc, output_plot])
    
    
    electoric_ploting.load(lambda: datetime.datetime.now(), 
                           None,
                           # c_time2,
                           every=3600)
    dep = electoric_ploting.load(plot_prediction_result, None, plot, every=3600)

electoric_ploting.launch()
    
plt.close()