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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())
with open('modeling_config.yaml', 'w', encoding='utf-8') as f:
config['A'] = 'A'
yaml.dump(config, f, encoding='utf-8', allow_unicode=True)
def retrain():
get_prediction_result(retrain = True)
def get_prediction_result(retrain = False):
today = datetime.datetime.now()
if retrain:
train_modeling.modeling()
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_train_newest_target_period():
df = pd.read_csv('data/train.csv')
train_max_yearmonth = pd.to_datetime(str(df['年月'].max()), format='%Y%m').strftime('%Y年%m月')
return train_max_yearmonth
def get_newest_target_period():
df = get_estat.get_household_survey()
expence_df = pd.DataFrame({'年月':df['時間軸（月次）'].unique()})
cate='3.1 電気代'
temp_df = df.loc[df['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)
target_max_yearmonth = pd.to_datetime(str(expence_df['年月'].max()), format='%Y%m').strftime('%Y年%m月')
return target_max_yearmonth
def get_model_infomation():
token = 'NjQwMDVmNGI0ZDQzZDFhYzI2YThmZDJiOnVZejljTXFNTXNoUnlKMStoUFhXSFdYMEZRck9lY3dobnEvRFZ1aVBHbVE9'
### デモ環境これっぽい
endpoint = 'https://app.datarobot.com/api/v2'
dr.Client(
endpoint=endpoint,
token=token
)
model_info = pd.read_csv('data/model_management.csv').iloc[-1, :]
model = dr.Model.get(project = dr.Project.get(model_info['model_url'].split('/')[4]), model_id = model_info['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, :]
return model_info, feature_impact
with gr.Blocks() as electoric_ploting:
print('out put')
pd.DataFrame({'A':[1]}).to_pickle('data.pickle')
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.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.Column():
gr.Textbox(get_train_newest_target_period,
label='現在の学習済みのターゲット値最新月')
gr.Textbox(get_newest_target_period,
label='現在の取得可能ターゲット値最新月')
btn= gr.Button(value="再学習")
btn.click(retrain, inputs=None, outputs=None)
with gr.Row():
model_info, feature_impact_df = get_model_infomation()
gr.Textbox(model_info['model_type'],
label='現在のモデル')
with gr.Row():
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)
# demo.load(make_plot, inputs=[button], outputs=[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.queue().launch()
plt.close()
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