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import numpy as np
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import pandas as pd
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import torch
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from torch import nn
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import os
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import matplotlib.pyplot as plt
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DAYS_FOR_TRAIN = 9
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class LSTM_Regression(nn.Module):
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def __init__(self, input_size, hidden_size, output_size=1, num_layers=2):
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super().__init__()
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self.lstm = nn.LSTM(input_size, hidden_size, num_layers)
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self.fc = nn.Linear(hidden_size, output_size)
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def forward(self, _x):
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x, _ = self.lstm(_x) # _x is input, size (seq_len, batch, input_size)
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s, b, h = x.shape # x is output, size (seq_len, batch, hidden_size)
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x = x.view(s * b, h)
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x = self.fc(x)
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x = x.view(s, b, -1) # 把形状改回来
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return x
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def create_dataset(data, days_for_train=5) -> (np.array, np.array):
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dataset_x, dataset_y = [], []
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for i in range(len(data) - days_for_train):
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_x = data[i:(i + days_for_train)]
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dataset_x.append(_x)
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dataset_y.append(data[i + days_for_train])
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return (np.array(dataset_x), np.array(dataset_y))
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if __name__ == '__main__':
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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data = pd.read_excel(r'C:\Users\user\Desktop\浙江21年-23年行业以及分电压等级日电量.xlsx', sheet_name=1)
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data.columns = data.columns.map(lambda x: x.strip())
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data.sort_values(by='pt_date', ascending=True)
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data['pt_date'] = pd.to_datetime(data['pt_date'], format='%Y%m%d', errors='coerce').astype('string')
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data.set_index('pt_date', inplace=True)
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data = data[(data.index.str[:6] != '202309') & (data.index.str[:6] != '202310')]
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data.drop(columns=[i for i in data.columns if (data[i] == 0).sum() / len(data) >= 0.5], inplace=True) # 去除0值列
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print('len(data):', len(data))
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for level in data.columns:
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df = data[level]
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df = df[df.values != 0] # 去除0值行
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df = df.astype('float32').values # 转换数据类型
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# 标准化到0~1
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max_value = np.max(df)
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min_value = np.min(df)
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df = (df - min_value) / (max_value - min_value)
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dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
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print('len(dataset_x:)', len(dataset_x))
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# 划分训练集和测试集
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train_size = len(dataset_x) - 31
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train_x = dataset_x[:train_size]
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train_y = dataset_y[:train_size]
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# 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size)
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train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
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train_y = train_y.reshape(-1, 1, 1)
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# 转为pytorch的tensor对象
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train_x = torch.from_numpy(train_x).to(device)
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train_y = torch.from_numpy(train_y).to(device)
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model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=1, num_layers=2).to(device) # 导入模型并设置模型的参数输入输出层、隐藏层等
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train_loss = []
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loss_function = nn.MSELoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
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for i in range(1200):
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out = model(train_x)
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loss = loss_function(out, train_y)
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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train_loss.append(loss.item())
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# 保存模型
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# torch.save(model.state_dict(),save_filename)
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# torch.save(model.state_dict(),os.path.join(model_save_dir,model_file))
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# for test
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model = model.eval() # 转换成测试模式
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# model.load_state_dict(torch.load(os.path.join(model_save_dir,model_file))) # 读取参数
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dataset_x = dataset_x.reshape(-1, 1, DAYS_FOR_TRAIN) # (seq_size, batch_size, feature_size)
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dataset_x = torch.from_numpy(dataset_x).to(device)
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pred_test = model(dataset_x) # 全量训练集
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# 模型输出 (seq_size, batch_size, output_size)
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pred_test = pred_test.view(-1).cpu().data.numpy()
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pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test))
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assert len(pred_test) == len(df)
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# 反归一化
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pred_test = pred_test * (max_value - min_value) + min_value
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df = df * (max_value - min_value) + min_value
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pred_test = pred_test.reshape(-1)
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df = df.reshape(-1)
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# 打印指标
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print(abs(pred_test[-31:] - df[-31:]).mean() / df[-31:].mean())
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result_eight = pd.DataFrame({'pred_test': pred_test[-31:], 'real': df[-31:]})
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target = (result_eight['pred_test'][-3:].sum() - result_eight['real'][-3:].sum()) / result_eight[
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'real'].sum()
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print(target)
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with open(fr'C:\Users\user\Desktop\电压等级电量预测.txt', 'a', encoding='utf-8') as f:
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f.write(f'{level}月底电量偏差率:{round(target, 5)}\n')
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