输出预测结果

main
鸽子 1 year ago
parent 6049161ed9
commit 765dba1ed1

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<?xml version="1.0" encoding="UTF-8"?> <?xml version="1.0" encoding="UTF-8"?>
<project version="4"> <project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="pytorch_gpu" project-jdk-type="Python SDK" /> <component name="ProjectRootManager" version="2" project-jdk-name="C:\anaconda\envs\pytorch" project-jdk-type="Python SDK" />
</project> </project>

@ -2,7 +2,7 @@
<module type="PYTHON_MODULE" version="4"> <module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager"> <component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" /> <content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="pytorch_gpu" jdkType="Python SDK" /> <orderEntry type="jdk" jdkName="C:\anaconda\envs\pytorch" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" /> <orderEntry type="sourceFolder" forTests="false" />
</component> </component>
</module> </module>

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import pandas as pd
import matplotlib.pyplot as plt
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
def normal(x):
high = x.describe()['75%'] + 1.5*(x.describe()['75%']-x.describe()['25%'])
low = x.describe()['25%'] - 1.5*(x.describe()['75%']-x.describe()['25%'])
return x[(x<=high)&(x>=low)]
def season(x):
if str(x)[5:7] in ('04', '05'):
return 0
elif str(x)[5:7] in ('01', '02', '03', '06', '09', '10', '11', '12'):
return 1
else:
return 2
df = pd.read_excel('./浙江各地市分电压日电量数据/杭州 .xlsx')
df = df[['stat_date','0.4kv及以下']]
df['0.4kv及以下'] = df['0.4kv及以下']/10000
df['stat_date'] = df['stat_date'].map(lambda x:x.strip())
df['stat_date'] = pd.to_datetime(df['stat_date'])
df_qw = pd.read_excel(r'C:\python-project\p1031\入模数据\杭州.xlsx')
df_qw.columns = df_qw.columns.map(lambda x:x.strip())
df_qw = df_qw[['dtdate','tem_max','tem_min','holiday','24ST']]
df_qw['dtdate'] = pd.to_datetime(df_qw['dtdate'])
df = pd.merge(df,df_qw,left_on='stat_date',right_on='dtdate',how='left')
df.drop(columns='dtdate',inplace=True)
df.set_index('stat_date',inplace=True)
df['season'] = df.index.map(season)
df = df.loc[normal(df['0.4kv及以下']).index]
print(df.head())
x_train = df.loc['2022-7':'2023-7'].drop(columns='0.4kv及以下')
y_train = df.loc['2022-7':'2023-7']['0.4kv及以下']
x_eval = df.loc['2023-8'].drop(columns='0.4kv及以下')
y_eval = df.loc['2023-8']['0.4kv及以下']
x_train,x_test,y_train,y_test = train_test_split(x_train,y_train,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)
print(r2_score(y_test,y_pred))
predict = model.predict(x_eval)
result = pd.DataFrame({'eval':y_eval,'pred':predict},index=y_eval.index)
print(result)
print((result['eval'][-3:].sum()-result['pred'][-3:].sum())/result['eval'].sum())
# 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}'
# print(df.loc[month_index]['0.4kv及以下'].max(),df['0.4kv及以下'].describe()['75%'])
# if df.loc[month_index]['0.4kv及以下'].mean() >= df['0.4kv及以下'].describe()['75%']:
# list2.append(i)
# elif df.loc[month_index]['0.4kv及以下'].mean() <= df['0.4kv及以下'].describe()['25%']:
# list0.append(i)
# else:
# list1.append(i)
# print(list0,list1,list2)

@ -0,0 +1,65 @@
import pandas as pd
import os
def normal(x):
high = x.describe()['75%'] + 1.5*(x.describe()['75%']-x.describe()['25%'])
low = x.describe()['25%'] - 1.5*(x.describe()['75%']-x.describe()['25%'])
return x[(x<=high)&(x>=low)]
fir_dir = './浙江各地市分电压日电量数据'
qw_dir = 'C:\python-project\p1031\入模数据'
result = pd.DataFrame({})
for excel,qw_excel in zip(os.listdir(fir_dir),os.listdir(qw_dir)):
df_city = pd.read_excel(os.path.join(fir_dir,excel))
df_city = df_city[['stat_date','0.4kv及以下']]
df_city['0.4kv及以下'] = df_city['0.4kv及以下']/10000
df_city['stat_date'] = df_city['stat_date'].map(lambda x:x.strip())
df_city['stat_date'] = pd.to_datetime(df_city['stat_date'])
df_qw = pd.read_excel(os.path.join(qw_dir,qw_excel))
df_qw.columns = df_qw.columns.map(lambda x:x.strip())
df_qw = df_qw[['dtdate','tem_max','tem_min','holiday','24ST']]
df_qw['dtdate'] = pd.to_datetime(df_qw['dtdate'])
df = pd.merge(df_city,df_qw,left_on='stat_date',right_on='dtdate',how='left')
df.drop(columns='dtdate',inplace=True)
df.set_index('stat_date',inplace=True)
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}'
# print(df.loc[month_index]['0.4kv及以下'].max(),df['0.4kv及以下'].describe()['75%'])
if df.loc[month_index]['0.4kv及以下'].mean() >= df['0.4kv及以下'].describe()['75%']:
list2.append(i)
elif df.loc[month_index]['0.4kv及以下'].mean() <= df['0.4kv及以下'].describe()['25%']:
list0.append(i)
else:
list1.append(i)
def season(x):
if str(x)[5:7] in list0:
return 0
elif str(x)[5:7] in list1:
return 1
else:
return 2
df['season'] = df.index.map(season)
dict1 = {'杭州':0,'湖州':1,'嘉兴':2,'金华':3,'丽水':4,'宁波':5,'衢州':6,'绍兴':7,'台州':8,'温州':9,'舟山':10}
df['city'] = dict1[excel[:2]]
df.reset_index(inplace=True)
result = pd.concat(result,df)
print(df)
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