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44 lines
1.7 KiB
Python
44 lines
1.7 KiB
Python
1 year ago
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import pandas as pd
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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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import numpy as np
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df = pd.read_excel(r'./400v入模数据.xlsx')
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df['stat_date'] = pd.to_datetime(df['stat_date'])
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print(df.corr()['0.4kv及以下'])
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X = df[(df['stat_date']>='2021-01-01')&(df['stat_date']<='2023-09-28')].drop(columns=['0.4kv及以下']).set_index('stat_date')
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y = df[(df['stat_date']>='2021-01-01')&(df['stat_date']<='2023-09-28')]['0.4kv及以下']
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x_eval = df[(df['stat_date']<='2023-09-30')&(df['stat_date']>='2023-09-01')].drop(columns=['0.4kv及以下']).set_index('stat_date')
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print(x_eval)
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y_eval = df[(df['stat_date']<='2023-09-30')&(df['stat_date']>='2023-09-01')][['0.4kv及以下','city']]
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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=250)
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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({'real':y_eval.drop(columns='city').values.reshape(-1),'pred':predict},index=x_eval.index)
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print(result.loc['2023-09-28':'2023-09-30'])
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dict2 = {'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}
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dict1 = {}
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for city in x_eval['city'].drop_duplicates():
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eval_x = x_eval[x_eval['city']==city]
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eval_y = y_eval[y_eval['city']==city]['0.4kv及以下']
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pred = model.predict(eval_x)
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loss_rate = (np.sum(pred[-3:])-np.sum(eval_y[-3:]))/np.sum(eval_y)
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dict1[city] = loss_rate
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for key in dict2.keys():
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dict2[key] = dict1[dict2[key]]
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print(dict2)
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