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138 lines
4.0 KiB
Python
138 lines
4.0 KiB
Python
import xgboost as xgb
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import pandas as pd
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import os
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from sklearn.metrics import r2_score
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from sklearn.model_selection import train_test_split
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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mpl.rcParams['font.sans-serif']=['kaiti']
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pd.set_option('display.width',None)
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def season(x):
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if str(x)[5:7] in ('01', '02'):
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return 0
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elif str(x)[5:7] in ('03', '04', '05', '06', '09', '10', '11', '12'):
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return 1
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else:
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return 2
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def normal(nd):
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high = nd.describe()['75%'] + 1.5*(nd.describe()['75%']-nd.describe()['25%'])
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low = nd.describe()['25%'] - 1.5*(nd.describe()['75%']-nd.describe()['25%'])
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return nd[(nd<high)&(nd>low)]
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parent_dir = os.path.abspath(os.path.join(os.getcwd(),os.pardir))
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data = pd.read_excel(os.path.join(parent_dir,'入模数据/衢州.xlsx'),index_col='dtdate')
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data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
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data = data.loc[normal(data['售电量']).index]
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# list2 = []
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# list0 = []
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# list1 = []
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# for i in ('01','02','03','04','05','06','07','08','09','10','11','12'):
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# month_index = data.index.strftime('%Y-%m-%d').str[5:7] == f'{i}'
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# if data.loc[month_index]['售电量'].mean() >= data['售电量'].describe()['75%']:
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# list2.append(i)
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# elif data.loc[month_index]['售电量'].mean() <= data['售电量'].describe()['25%']:
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# list0.append(i)
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# else:
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# list1.append(i)
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#
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# print(list0,list1,list2)
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data['season'] = data.index.map(season)
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df_eval = data.loc['2023-08']
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df_train = data.iloc[450:900]
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df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
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X = df_train[['tem_max','tem_min','holiday','24ST','season']]
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X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
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y = df_train['售电量']
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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=0.1, 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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result_test = pd.DataFrame({'test':y_test,'pred':y_pred},index=y_test.index)
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# 指标打印
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eval_pred = model.predict(X_eval)
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result_eval = pd.DataFrame({'eval':df_eval['售电量'],'pred':eval_pred})
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goal = (result_eval['eval'][-3:].sum()-result_eval['pred'][-3:].sum())/result_eval['eval'].sum()
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print(goal)
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goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
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print(goal2)
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# 保存模型
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model.save_model('quzhou.bin')
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loaded_model = xgb.XGBRegressor()
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loaded_model.load_model('quzhou.bin')
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import numpy as np
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X_eval = np.array([[23.69,16.5,23,1,0],
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[24.5,15.19,23,0,0],
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[25.19,13.9,23,0,0]])
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print(model.predict(X_eval))
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# import torch
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# from torch import nn
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# from torch.utils.data import TensorDataset,DataLoader
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#
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#
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# net = nn.Sequential(
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# nn.Linear(6,32),
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# nn.ReLU(),
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# nn.Linear(32,64),
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# nn.ReLU(),
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# nn.Linear(64,64),
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# nn.ReLU(),
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# nn.Linear(64,1)
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# )
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# opt = torch.optim.Adam(net.parameters(),lr=0.00005)
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# loss_fn = nn.MSELoss()
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#
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# epochs = 200
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#
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# x_train = torch.from_numpy(x_train.values).type(torch.float32)
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# x_train = (x_train - x_train.mean())/x_train.std()
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#
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# y_train = torch.from_numpy(y_train.values).type(torch.float32)
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# std1 = y_train.std()
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# mean1 = y_train.mean()
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# y_train = (y_train - mean1)/std1
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#
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# X_eval = torch.from_numpy(X_eval.values).type(torch.float32)
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# X_eval = (X_eval - X_eval.mean())/X_eval.std()
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#
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# y_eval= torch.from_numpy(df_eval['售电量'].values).type(torch.float32)
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#
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#
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# train_ds = TensorDataset(x_train,y_train)
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# train_dl = DataLoader(train_ds,shuffle=True,batch_size=64)
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#
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#
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# for i in range(epochs):
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# for x,y in train_dl:
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# y_pred = net(x)
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# loss = loss_fn(y_pred,y)
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#
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# opt.zero_grad()
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# loss.backward()
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# opt.step()
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# print(round(loss.item(),2))
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#
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# predict = (net(X_eval) * std1 + mean1).detach().numpy()
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# print(y_train.std(),y_train.mean())
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# print(net(X_eval))
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# print(predict)
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# print((y_eval.detach().numpy().sum() - predict.sum())/ y_eval.detach().numpy().sum())
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