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