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@ -3,6 +3,9 @@ import pandas as pd
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import torch
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import torch
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from torch import nn
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from torch import nn
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import os
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import os
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import time
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t1 = time.time()
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
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DAYS_FOR_TRAIN = 10
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DAYS_FOR_TRAIN = 10
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torch.manual_seed(42)
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torch.manual_seed(42)
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@ -38,8 +41,7 @@ def normal(nd):
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def data_preprocessing(data):
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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.columns = data.columns.map(lambda x: x.strip())
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data.index = data.index.map(lambda x:x.strip())
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data.index = data.index.map(lambda x:str(x).strip()[:10])
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data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
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data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
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data.sort_index(inplace=True)
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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 = data.loc['2021-01':'2023-08']
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@ -52,78 +54,78 @@ def data_preprocessing(data):
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return data
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return data
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# 拼接数据集
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# 拼接数据集
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file_dir = r'C:\Users\user\Desktop\浙江各地市行业电量数据'
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# file_dir = './浙江各地市行业电量数据'
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excel = os.listdir(file_dir)[0]
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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 = 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.drop(columns='地市',inplace=True)
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data = data_preprocessing(data)
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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 = data[data.columns[0]]
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df.dropna(inplace = True)
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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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# 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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# for level in data.columns[1:]:
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df = data[level]
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# df = data[level]
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df.dropna(inplace=True)
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# df.dropna(inplace=True)
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x, y = create_dataset(df, DAYS_FOR_TRAIN)
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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_x = np.concatenate((dataset_x, x))
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dataset_y = np.concatenate((dataset_y, y))
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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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# 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 = 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.drop(columns='地市', inplace=True)
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data = data_preprocessing(data)
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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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# for level in data.columns:
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df = data[level]
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# df = data[level]
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df.dropna(inplace=True)
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# df.dropna(inplace=True)
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x,y = create_dataset(df,DAYS_FOR_TRAIN)
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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_x = np.concatenate((dataset_x,x))
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dataset_y = np.concatenate((dataset_y,y))
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# dataset_y = np.concatenate((dataset_y,y))
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#
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#
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print(dataset_x.shape,dataset_y.shape)
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# print(dataset_x.shape,dataset_y.shape)
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# # 训练
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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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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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#
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#
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# # 标准化到0~1
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# # 标准化到0~1
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max_value = np.max(dataset_x)
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# max_value = np.max(dataset_x)
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min_value = np.min(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_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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# dataset_y = (dataset_y - min_value) / (max_value - min_value)
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print('max_value:',max_value,'min_value:',min_value)
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# print('max_value:',max_value,'min_value:',min_value)
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# 划分训练集和测试集
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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_size = int(len(dataset_x)*0.7)
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train_x = dataset_x[:train_size]
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# train_x = dataset_x[:train_size]
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train_y = dataset_y[: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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# # # 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_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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# 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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# # # 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_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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# train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
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#
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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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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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#
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train_loss = []
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# train_loss = []
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loss_function = nn.MSELoss()
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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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# 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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# for i in range(1500):
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out = model(train_x)
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# out = model(train_x)
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loss = loss_function(out, train_y)
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# loss = loss_function(out, train_y)
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loss.backward()
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# loss.backward()
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optimizer.step()
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# optimizer.step()
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optimizer.zero_grad()
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# optimizer.zero_grad()
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train_loss.append(loss.item())
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# train_loss.append(loss.item())
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if i % 100 == 0:
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# if i % 100 == 0:
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print(f'epoch {i+1}: loss:{loss}')
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# print(f'epoch {i+1}: loss:{loss}')
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#
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# 保存/读取模型
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# # 保存/读取模型
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torch.save(model.state_dict(),'hy5.pth')
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# torch.save(model.state_dict(),'hy5.pth')
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# model.load_state_dict(torch.load('hy5.pth'))
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# model.load_state_dict(torch.load('hy5.pth'))
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# # for test
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# # for test
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@ -148,7 +150,7 @@ max_value = 354024930.8
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min_value = 0.0
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min_value = 0.0
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# 创建测试集
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# 创建测试集
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file_dir = r'C:\Users\鸽子\Desktop\浙江各地市行业电量数据'
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file_dir = './浙江各地市行业电量数据'
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for excel in os.listdir(file_dir):
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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 = pd.read_excel(os.path.join(file_dir,excel))
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city = df_city['地市'].iloc[0]
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city = df_city['地市'].iloc[0]
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@ -164,7 +166,8 @@ for excel in os.listdir(file_dir):
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result = pred.detach().numpy()[-5:-2]
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result = pred.detach().numpy()[-5:-2]
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result_dict[industry] = list(result)
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result_dict[industry] = 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 = pd.DataFrame(result_dict,index=['2023-10-29','2023-10-30','2023-10-31'])
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df.to_excel(fr'C:\Users\鸽子\Desktop\行业电量预测29-31\{city}.xlsx')
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# df.to_excel(fr'./行业电量预测29-31/{city}.xlsx')
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print(time.time()-t1)
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print(result_dict)
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print(result_dict)
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# 反归一化
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# 反归一化
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