import altair import gradio as gr from math import sqrt import pandas as pd import numpy as np from datetime import datetime import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt # # pip install beautifulsoup4 # # pip install requests_html # import requests # from bs4 import BeautifulSoup as bs # from requests_html import AsyncHTMLSession # # Webページを取得して解析する # load_url = "https://www.football-lab.jp/kyot/match/" # html = requests.get(load_url) # soup = bs(html.content, "html.parser") # url23 = 'https://www.football-lab.jp/ka-f/match/' # dfs23 = pd.read_html(url23) # url22 = 'https://www.football-lab.jp/ka-f/match/?year=2022' # dfs22 = pd.read_html(url22) # url21 = 'https://www.football-lab.jp/ka-f/match/?year=2021' # dfs21 = pd.read_html(url21) # url20 = 'https://www.football-lab.jp/ka-f/match/?year=2020' # dfs20 = pd.read_html(url20) # #シーズン毎に分類 # res23 = pd.DataFrame([['S2023']]*len(dfs23[0])).join(dfs23, lsuffix='0') # res22 = pd.DataFrame([['S2022']]*len(dfs22[0])).join(dfs22, lsuffix='0') # res21 = pd.DataFrame([['S2021']]*len(dfs21[0])).join(dfs21, lsuffix='0') # res20 = pd.DataFrame([['S2020']]*len(dfs20[0])).join(dfs20, lsuffix='0') # df_tmp = pd.concat([res23, res22, res21, res20]) # df = df_tmp # df = df.rename(columns={'会場': 'stadium', 0: 'year', '開催日': 'date', '観客数': 'audience'}) # df = df.query('stadium=="等々力"').reset_index() # df = df.query('audience.notna()', engine='python').reset_index() # df = df[['audience', 'year', 'date']] # #seasonカラムから年を抽出 # df["year"] = df["year"].apply(lambda x: str(x)[1:5]) # #開催日から月と日を分割 # df['month'] = df['date'].str.split(pat='.', expand=True)[0] # df['day'] = df['date'].str.split(pat='.', expand=True)[1] # #数値データを日付データに変換 # df['date'] = pd.to_datetime({'year': df['year'], 'month': df['month'], 'day': df['day']}) # #日付昇順に並び替える # df = df.sort_values('date', ascending=True) # df['date_ymd'] = pd.to_datetime(df['date']).dt.strftime('%Y%m%d') # df['date_ym'] = pd.to_datetime(df['date']).dt.strftime('%Y%m') # df["date_ymd"] = df["date_ymd"].astype(int) # df['date_before'] = df['date_ymd'] - 1 # df["date_before"] = df["date_before"] # df = df[['audience', 'date_ymd', 'date_before']] # df['last_audience'] = df['audience'].shift(1) # df_aji = pd.read_csv('fish_price.csv') # df_train = pd.merge(df, df_aji, left_on='date_before', right_on='date', how='left') # df_train = df_train.query('date > 20201202') # df_train = df_train.drop(['date_before', 'date_ymd'], axis=1) # df_train["audience"] = df_train["audience"].str.replace(",", "").astype(int) # df_train["last_audience"] = df_train["last_audience"].str.replace(",", "").astype(int) df_train = pd.read_csv('df_train.csv') X = df_train.drop('audience', axis=1) y = df_train['audience'] from sklearn.linear_model import LinearRegression from sklearn.metrics import log_loss from sklearn.preprocessing import StandardScaler linear_regression = LinearRegression() linear_regression.fit(X,y) def outbreak(date): if date: if __name__ == "__main__": start_date = d_today end_date = d_tom df_aji_pre = get_fish_price_data(start_date=start_date, end_date=end_date) # df_aji_pre.to_csv("fish_price_pre.csv", index=False) df_pre = df.tail(1).reset_index() df_pre = df_pre.drop('index', axis=1) df_aji_ft_pre = pd.concat([df_pre, df_aji_pre], axis=1) df_aji_ft_pre = df_aji_ft_pre[['audience', 'date', 'low_price', 'center_price', 'high_price', 'quantity']] df_aji_ft_pre = df_aji_ft_pre.rename(columns={'audience': 'last_audience', 0: 'year', '開催日': 'date', '観客数': 'audience'}) pred = linear_regression.predict(df_aji_ft_pre) df_aji_ft_pre['audience_pred'] = pred df_aji_ft_pre['date'] = df_aji_ft_pre['date'].astype(int) fig = plt.figure() plt.plot(df_train['date'], df_train['audience'], label='original') plt.plot(df_aji_ft_pre['date'], df_aji_ft_pre['audience_pred'], '*', label='predict') plt.title("prediction of audince") plt.ylabel("audience") plt.xlabel("Days since Day 0") return fig with gr.Blocks() as demo: gr.Markdown( """ # 川崎フロンターレの観客動員数の予測 川崎フロンターレの等々力陸上競技場での試合の観客数を「あじ」の価格をもとに予測する。 ## 使用データ * 東京卸売市場日報 * Football Lab ## 予測ロジック 観客動員数は雨天か否かで左右されると考えられる。そこで雨天の可能性をあじの価格を利用し表した。 一般的に雨天の場合、低気圧の影響で海面が上昇し漁に出ることが難しくなる。 そのため漁獲量が減少し、あじの価格が上昇すると考えられる。 """ ) with gr.Row(): with gr.Column(): date_input = gr.Checkbox(label='please input date') prediction_btn = gr.Button(value="predict") with gr.Column(): prediction = gr.Plot(label = "時系列プロット") prediction_btn.click(outbreak, inputs=date_input, outputs=prediction) demo.launch()