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get 1 year ago
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commit 6e775ae4d9
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浙江行业电量/行业电量_输出为5.py
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@ -54,91 +54,91 @@ def data_preprocessing(data):
return data
# 拼接数据集
file_dir = r'C:\Users\user\Desktop\浙江各地市行业电量数据'
excel = os.listdir(file_dir)[0]
data = pd.read_excel(os.path.join(file_dir, excel), sheet_name=0, index_col='stat_date')
data.drop(columns='地市',inplace=True)
data = data_preprocessing(data)
df = data[data.columns[0]]
df.dropna(inplace = True)
dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
for level in data.columns[1:]:
df = data[level]
df.dropna(inplace=True)
x, y = create_dataset(df, DAYS_FOR_TRAIN)
dataset_x = np.concatenate((dataset_x, x))
dataset_y = np.concatenate((dataset_y, y))
for excel in os.listdir(file_dir)[1:]:
data = pd.read_excel(os.path.join(file_dir,excel), sheet_name=0,index_col='stat_date')
data.drop(columns='地市', inplace=True)
data = data_preprocessing(data)
for level in data.columns:
df = data[level]
df.dropna(inplace=True)
x,y = create_dataset(df,DAYS_FOR_TRAIN)
dataset_x = np.concatenate((dataset_x,x))
dataset_y = np.concatenate((dataset_y,y))
print(dataset_x.shape,dataset_y.shape)
# 训练
# file_dir = r'C:\Users\user\Desktop\浙江各地市行业电量数据'
# excel = os.listdir(file_dir)[0]
# data = pd.read_excel(os.path.join(file_dir, excel), sheet_name=0, index_col='stat_date')
# data.drop(columns='地市',inplace=True)
# data = data_preprocessing(data)
#
# df = data[data.columns[0]]
# df.dropna(inplace = True)
# dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
#
# for level in data.columns[1:]:
# df = data[level]
# df.dropna(inplace=True)
# x, y = create_dataset(df, DAYS_FOR_TRAIN)
# dataset_x = np.concatenate((dataset_x, x))
# dataset_y = np.concatenate((dataset_y, y))
#
#
# for excel in os.listdir(file_dir)[1:]:
#
# data = pd.read_excel(os.path.join(file_dir,excel), sheet_name=0,index_col='stat_date')
# data.drop(columns='地市', inplace=True)
# data = data_preprocessing(data)
#
# for level in data.columns:
# df = data[level]
# df.dropna(inplace=True)
# x,y = create_dataset(df,DAYS_FOR_TRAIN)
# dataset_x = np.concatenate((dataset_x,x))
# dataset_y = np.concatenate((dataset_y,y))
#
#
# print(dataset_x.shape,dataset_y.shape)
# # 训练
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 标准化到0~1
max_value = np.max(dataset_x)
min_value = np.min(dataset_x)
dataset_x = (dataset_x - min_value) / (max_value - min_value)
dataset_y = (dataset_y - min_value) / (max_value - min_value)
# 划分训练集和测试集
train_size = int(len(dataset_x)*0.7)
train_x = dataset_x[:train_size]
train_y = dataset_y[:train_size]
# 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size)
train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
train_y = train_y.reshape(-1, 1, 5)
# 转为pytorch的tensor对象
train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
#
# # 标准化到0~1
# max_value = np.max(dataset_x)
# min_value = np.min(dataset_x)
# dataset_x = (dataset_x - min_value) / (max_value - min_value)
# dataset_y = (dataset_y - min_value) / (max_value - min_value)
# print('max_value:',max_value,'min_value:',min_value)
# # 划分训练集和测试集
# train_size = int(len(dataset_x)*0.7)
# train_x = dataset_x[:train_size]
# train_y = dataset_y[:train_size]
#
# # 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size)
# train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
# train_y = train_y.reshape(-1, 1, 5)
#
# # 转为pytorch的tensor对象
# train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
# train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
#
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
# train_loss = []
# loss_function = nn.MSELoss()
# optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
# for i in range(1500):
# out = model(train_x)
# loss = loss_function(out, train_y)
# loss.backward()
# optimizer.step()
# optimizer.zero_grad()
# train_loss.append(loss.item())
# if i % 100 == 0:
# print(f'epoch {i+1}: loss:{loss}')
# 保存/读取模型
# torch.save(model.state_dict(),'hy5.pth')
model.load_state_dict(torch.load('hy5.pth'))
# for test
model = model.eval() # 转换成测试模式
# model.load_state_dict(torch.load(os.path.join(model_save_dir,model_file))) # 读取参数
dataset_x = dataset_x.reshape(-1, 1, DAYS_FOR_TRAIN) # (seq_size, batch_size, feature_size)
dataset_x = torch.from_numpy(dataset_x).to(device).type(torch.float32)
pred_test = model(dataset_x) # 全量训练集
# 模型输出 (seq_size, batch_size, output_size)
pred_test = pred_test.view(-1)
pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().numpy()))
#
#
# # train_loss = []
# # loss_function = nn.MSELoss()
# # optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
# # for i in range(1500):
# # out = model(train_x)
# # loss = loss_function(out, train_y)
# # loss.backward()
# # optimizer.step()
# # optimizer.zero_grad()
# # train_loss.append(loss.item())
# # if i % 100 == 0:
# # print(f'epoch {i+1}: loss:{loss}')
#
# # 保存/读取模型
# # torch.save(model.state_dict(),'hy5.pth')
#
# model.load_state_dict(torch.load('hy5.pth'))
# # for test
# model = model.eval() # 转换成测试模式
# # model.load_state_dict(torch.load(os.path.join(model_save_dir,model_file))) # 读取参数
# dataset_x = dataset_x.reshape(-1, 1, DAYS_FOR_TRAIN) # (seq_size, batch_size, feature_size)
# dataset_x = torch.from_numpy(dataset_x).to(device).type(torch.float32)
#
# pred_test = model(dataset_x) # 全量训练集
# # 模型输出 (seq_size, batch_size, output_size)
# pred_test = pred_test.view(-1)
# pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().numpy()))
# plt.plot(pred_test.reshape(-1), 'r', label='prediction')
# plt.plot(dataset_y.reshape(-1), 'b', label='real')
@ -146,6 +146,9 @@ pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().n
# plt.legend(loc='best')
# plt.show()
model.load_state_dict(torch.load('hy5.pth'))
max_value = 354024930.8
min_value = 0.0
# 创建测试集
@ -153,7 +156,7 @@ df_eval = pd.read_excel(r'C:\Users\user\Desktop\浙江各地市行业电量数
df_eval.columns = df_eval.columns.map(lambda x:x.strip())
df_eval.index = pd.to_datetime(df_eval.index)
x,y = create_dataset(df_eval.loc['2023-7']['第三产业'],10)
x,y = create_dataset(df_eval.loc['2023-7']['第二产业'],10)
x = (x - min_value) / (max_value - min_value)
x = x.reshape(-1,1,10)
@ -161,13 +164,21 @@ x = x.reshape(-1,1,10)
x = torch.from_numpy(x).type(torch.float32).to(device)
pred = model(x)
x2 = np.array([227964890.1,220189256.2,220189256.2,220189256.2,220189256.2,220189256.2,220189256.2,220189256.2,220189256.2,220189256.2])
x2 = (x2 - min_value) / (max_value - min_value)
x2 = x2.reshape(-1,1,10)
print(x2)
x2 = torch.from_numpy(x2).type(torch.float32).to(device)
pred2 = model(x2)
# 反归一化
pred = pred * (max_value - min_value) + min_value
pred2 = pred2 * (max_value - min_value) + min_value
print('pred2:',pred2.view(-1).cpu().detach().numpy())
# df = df * (max_value - min_value) + min_value
df = pd.DataFrame({'real':y.reshape(-1),'pred':pred.view(-1).cpu().detach().numpy()})
print(df)
df.to_csv('7月第三产业.csv',encoding='gbk')
df.to_csv('7月第二产业.csv',encoding='gbk')
# 反归一化
# pred = pred * (max_value - min_value) + min_value

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