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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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from multiprocessing import Pool
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import matplotlib.pyplot as plt
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import os
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" # 解决OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
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DAYS_FOR_TRAIN = 10
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torch.manual_seed(42)
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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-5):
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dataset_x.append(data[i:(i + days_for_train)])
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dataset_y.append(data[i + days_for_train:i + days_for_train+5])
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# print(dataset_x,dataset_y)
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return (np.array(dataset_x), np.array(dataset_y))
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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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def data_preprocessing(data):
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data.columns = data.columns.map(lambda x: x.strip())
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data.index = pd.to_datetime(data.index)
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data.sort_index(inplace=True)
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data = data.loc['2021-01':'2023-08']
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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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data = data.astype(float)
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for col in data.columns:
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data[col] = normal(data[col])
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return data
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# 拼接数据集
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# file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据'
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# excel = os.listdir(file_dir)[0]
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# data = pd.read_excel(os.path.join(file_dir, excel), sheet_name=0, index_col='stat_date')
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# data.drop(columns='地市',inplace=True)
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# data = data_preprocessing(data)
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#
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# df = data[data.columns[0]]
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# df.dropna(inplace = True)
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# dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
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#
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# for level in data.columns[1:]:
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# df = data[level]
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# df.dropna(inplace=True)
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# x, y = create_dataset(df, DAYS_FOR_TRAIN)
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# dataset_x = np.concatenate((dataset_x, x))
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# dataset_y = np.concatenate((dataset_y, y))
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#
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#
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# for excel in os.listdir(file_dir)[1:]:
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#
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# data = pd.read_excel(os.path.join(file_dir,excel), sheet_name=0,index_col='stat_date')
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# data.drop(columns='地市', inplace=True)
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# data = data_preprocessing(data)
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#
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# for level in data.columns:
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# df = data[level]
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# df.dropna(inplace=True)
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# x,y = create_dataset(df,DAYS_FOR_TRAIN)
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# dataset_x = np.concatenate((dataset_x,x))
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# dataset_y = np.concatenate((dataset_y,y))
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#
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#
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#
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# # 训练
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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#
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# # 标准化到0~1
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# max_value = np.max(dataset_x)
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# min_value = np.min(dataset_x)
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# dataset_x = (dataset_x - min_value) / (max_value - min_value)
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# dataset_y = (dataset_y - min_value) / (max_value - min_value)
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#
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# print(max_value,min_value)
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# # 划分训练集和测试集
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# train_size = int(len(dataset_x)*0.7)
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# train_x = dataset_x[:train_size]
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# train_y = dataset_y[:train_size]
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#
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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, 5)
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#
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# # 转为pytorch的tensor对象
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# train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
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# train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
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model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=5, 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=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
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# for i in range(1500):
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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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#
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# # 保存模型
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# torch.save(model.state_dict(),'dy5.pth')
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#
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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).type(torch.float32)
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#
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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)
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# pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().numpy()))
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#
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# plt.plot(pred_test.reshape(-1), 'r', label='prediction')
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# plt.plot(dataset_y.reshape(-1), 'b', label='real')
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# plt.plot((train_size*5, train_size*5), (0, 1), 'g--') # 分割线 左边是训练数据 右边是测试数据的输出
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# plt.legend(loc='best')
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# plt.show()
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# 创建测试集
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max_value,min_value = 192751288.47,0.0
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model.load_state_dict(torch.load('dy5.pth',map_location=torch.device('cpu')))
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file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据'
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for excel in os.listdir(file_dir):
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df_city = pd.read_excel(os.path.join(file_dir,excel))
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df_city.drop(columns=[i for i in df_city.columns if (df_city[i] == 0).sum() / len(df_city) >= 0.5], inplace=True)
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city = df_city['地市'].iloc[0]
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result_dict = {}
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for level in df_city.columns[2:]:
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x, y = create_dataset(df_city[level], 10)
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x = (x - min_value) / (max_value - min_value)
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x = x.reshape(-1, 1, 10)
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x = torch.from_numpy(x).type(torch.float32).to(device)
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pred = model(x).view(-1)
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pred = pred * (max_value - min_value) + min_value
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result = pred.cpu().detach().numpy()[-5:-2]
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result_dict[level] = list(result)
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df = pd.DataFrame(result_dict,index=['2023-10-29','2023-10-30','2023-10-31'])
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df.to_excel(fr'C:\Users\user\Desktop\分压电量预测29-31\{city} .xlsx')
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print(result_dict)
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# # 打印指标
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# print(abs(pred - df[-3:]).mean() / df[-3:].mean())
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# result_eight = pd.DataFrame({'pred': np.round(pred,1),'real': df[-3:]})
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# target = (result_eight['pred'].sum() - result_eight['real'].sum()) / df[-31:].sum()
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# result_eight['loss_rate'] = round(target, 5)
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# result_eight['level'] = level
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# list_app.append(result_eight)
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# print(target)
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# print(result_eight)
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# final_df = pd.concat(list_app,ignore_index=True)
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# final_df.to_csv('市行业电量.csv',encoding='gbk')
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# print(final_df)
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