pytorch/区域电量19年至今数据.py

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[(nd<high)&(nd>low)].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)