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import xgboost as xgb
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import pandas as pd
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import os
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from sklearn.metrics import r2_score
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from sklearn.model_selection import train_test_split
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import datetime
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import math
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from sklearn.preprocessing import LabelEncoder
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mpl.rcParams['font.sans-serif']=['kaiti']
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pd.set_option('display.width',None)
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def season(x):
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if str(x)[5:7] in ['07', '08']:
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return 2
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elif str(x)[5:7] in ['01', '02', '03', '06', '09', '11', '12']:
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return 1
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elif str(x)[5:7] in['04', '05', '10']:
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return 0
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def normal(nd):
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high = nd.describe()['75%'] + 1.5*(nd.describe()['75%']-nd.describe()['25%'])
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low = nd.describe()['25%'] - 1.5*(nd.describe()['75%']-nd.describe()['25%'])
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return nd[(nd<high)&(nd>low)].index
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# df = pd.read_excel(r'C:\Users\鸽子\Desktop\杭州19年至今日电量及气象数据.xlsx',sheet_name=0)
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# df_elec = pd.read_excel(r'C:\Users\鸽子\Desktop\杭州19年至今日电量及气象数据.xlsx',sheet_name=1)
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# df_elec.columns = df_elec.columns.map(lambda x:x.strip())
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# df_elec['售电量'] = df_elec['售电量']/10000
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# df.columns = df.columns.map(lambda x:x.strip())
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# df = df[['dtdate','tem_max','tem_min']]
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# # print(df.head())
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# # print(df_elec.head())
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#
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# merge_df = pd.merge(df_elec,df,left_on='pt_date',right_on='dtdate')[['pt_date','tem_max','tem_min','售电量']]
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# merge_df.set_index('pt_date',inplace=True)
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# merge_df.index = pd.to_datetime(merge_df.index,format='%Y%m%d')
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#
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#
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# merge_df['month'] = merge_df.index.strftime('%Y-%m-%d').str[5:7]
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# merge_df['month'] = merge_df['month'].astype('int')
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# merge_df.to_csv('杭州入模数据.csv',encoding='gbk')
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data = pd.read_csv(r'杭州入模数据.csv',encoding='gbk')
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data.drop_duplicates(subset='pt_date',inplace=True)
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data.set_index('pt_date',inplace=True)
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data.index = pd.to_datetime(data.index)
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print(data.loc['2023-07'])
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def jq(y,x):
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a=365.242 * (y - 1900) + 6.2 + 15.22 * x - 1.9 * math.sin(0.262 * x)
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return datetime.date(1899,12,31)+datetime.timedelta(days=int(a))
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# print(jq(2020,0))
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jq_list=['小寒', '大寒', '立春', '雨水', '惊蛰', '春分', '清明', '谷雨', '立夏', '小满', '芒种', '夏至', '小暑', '大暑', '立秋', '处暑', '白露', '秋分', '寒露', '霜降', '立冬', '小雪', '大雪','冬至']
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jq_dict={}
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for j in range(2019,2024):
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for i in range(24):
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jq_dict[jq(j,i).strftime('%Y-%m-%d')]=jq_list[i]
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# print(jq_dict)
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data['24ST']=data.index
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data['24ST']=data['24ST'].astype('string').map(jq_dict)
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data['24ST'].fillna(method='ffill',inplace=True)
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data['24ST'].fillna('冬至',inplace=True)
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# data为数据集 product_tags为需要编码的特征列(假设为第一列)
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le = LabelEncoder()
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data['24ST'] = le.fit_transform(data['24ST'])
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data = data.loc[normal(data['售电量'])]
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data['season'] = data.index.map(season)
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print(data['售电量'].describe())
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print(data)
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# list2 = []
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# list0 = []
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# list1 = []
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# for i in ('01','02','03','04','05','06','07','08','09','10','11','12'):
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# month_index = df.index.strftime('%Y-%m-%d').str[5:7] == f'{i}'
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# if df.loc[month_index]['售电量'].mean() >= df['售电量'].describe()['75%']:
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# list2.append(i)
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# elif df.loc[month_index]['售电量'].mean() <= df['售电量'].describe()['25%']:
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# list0.append(i)
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# else:
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# list1.append(i)
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# print(list0,list1,list2)
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# data = pd.read_excel(r'C:\python-project\pytorch3\入模数据\杭州数据.xlsx',index_col='dtdate')
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# data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
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# data = data.loc[normal(data['售电量']).index]
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# plt.plot(range(len(data['售电量']['2021':'2022'])),data['售电量']['2021':'2022'])
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# plt.show()
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# # print(hf_season(data.loc['2021']['售电量']))
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# data['month'] = data.index.strftime('%Y-%m-%d').str[6]
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# data['month'] = data['month'].astype('int')
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# data['season'] = data.index.map(season)
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# print(data.head(50))
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#
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df_eval = data.loc['2023-9']
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df_train = data.loc['2019-1':'2023-8']
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print(len(df_train),len(df_eval))
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plt.plot(range(len(data.loc['2019-1':'2023-9'])),data.loc['2019-1':'2023-9'])
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plt.show()
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# df_train = df[500:850]
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print(len(df_eval),len(df_train),len(data))
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print(data.corr(method='pearson')['售电量'])
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df_train = df_train[['tem_max','tem_min','24ST','售电量','season']]
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# IQR = df['售电量'].describe()['75%'] - df['售电量'].describe()['25%']
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# high = df['售电量'].describe()['75%'] + 1.5*IQR
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# low = df['售电量'].describe()['25%'] - 1.5*IQR
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# print('异常值数量：',len(df[(df['售电量'] >= high) | (df['售电量'] <= low)]))
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#
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# df_train = df_train[(df['售电量'] <= high) & (df['售电量'] >= low)]
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X = df_train[['tem_max','tem_min','season','24ST']]
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X_eval = df_eval[['tem_max','tem_min','season','24ST']]
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y = df_train['售电量']
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print(y.describe())
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# best_goal = 1
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# best_i = {}
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# for i in range(400):
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x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
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model = xgb.XGBRegressor(max_depth=6, learning_rate=0.05, n_estimators=150)
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model.fit(x_train,y_train)
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y_pred = model.predict(x_test)
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result_test = pd.DataFrame({'test':y_test,'pred':y_pred},index=y_test.index)
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# 指标打印
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print(abs(y_test - y_pred).mean() / y_test.mean())
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eval_pred = model.predict(X_eval)
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result_eval = pd.DataFrame({'eval':df_eval['售电量'],'pred':eval_pred},index=df_eval['售电量'].index)
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print((result_eval['eval'].sum()-result_eval['pred'].sum())/result_eval['eval'].sum())
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goal = (result_eval['eval'][-3:].sum()-result_eval['pred'][-3:].sum())/result_eval['eval'].sum()
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print('goal:',goal)
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goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
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print('goal2:',goal2)
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print(result_eval)
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print('r2:',r2_score(y_test,y_pred))
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# if abs(goal) < best_goal:
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# best_goal = abs(goal)
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# best_i['best_i'] = i
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# x = goal2
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# print(best_i,best_goal,x)
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# result_eval.to_csv(r'C:\Users\user\Desktop\9月各地市日电量预测结果\杭州.csv')
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# with open(r'C:\Users\user\Desktop\9月各地市日电量预测结果\偏差率.txt','a',encoding='utf-8') as f:
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# f.write(f'杭州月末3天偏差率：{round(goal,5)},9号-月底偏差率：{round(goal2,5)}\n')
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# # 保存模型
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# model.save_model('hangzhou.bin')
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# loaded_model = xgb.XGBRegressor()
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# loaded_model.load_model('hangzhou.bin')
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# model.predict(X_eval)
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