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41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
1 year ago
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import pandas as pd
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import matplotlib.pyplot as plt
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import xgboost as xgb
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import r2_score
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def normal(x):
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high = x.describe()['75%'] + 1.5*(x.describe()['75%']-x.describe()['25%'])
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low = x.describe()['25%'] - 1.5*(x.describe()['75%']-x.describe()['25%'])
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return x[(x<=high)&(x>=low)]
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def season(x):
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if str(x)[5:7] in ('04', '05', '06', '11'):
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return 0
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elif str(x)[5:7] in ('01', '02', '03', '09', '10', '12'):
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return 1
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else:
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return 2
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df = pd.read_excel('../400v入模数据/衢州.xlsx',index_col='stat_date')
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df.index = pd.to_datetime(df.index)
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x_train = df.loc['2021-7':'2023-7'][:-3].drop(columns='0.4kv及以下')
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y_train = df.loc['2021-7':'2023-7'][:-3]['0.4kv及以下']
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x_eval = df.loc['2023-7'].drop(columns='0.4kv及以下')
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y_eval = df.loc['2023-7']['0.4kv及以下']
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x_train,x_test,y_train,y_test = train_test_split(x_train,y_train,test_size=0.2,random_state=142)
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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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print(r2_score(y_test,y_pred))
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predict = model.predict(x_eval)
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result = pd.DataFrame({'eval':y_eval,'pred':predict},index=y_eval.index)
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print(result)
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print((result['eval'][-3:].sum()-result['pred'][-3:].sum())/result['eval'].sum())
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