From f8969d4f06f01271ca3dbc70a4e9e1b9caf05ec3 Mon Sep 17 00:00:00 2001 From: get <2316994765@qq.com> Date: Tue, 31 Oct 2023 22:20:44 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E7=94=B5=E5=8E=8Blstm?= =?UTF-8?q?=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 浙江电压等级电量/test1.py | 1 + .../电压等级_输出为5.py | 211 +++++++++--------- 2 files changed, 106 insertions(+), 106 deletions(-) diff --git a/浙江电压等级电量/test1.py b/浙江电压等级电量/test1.py index 56df96e..afd73f9 100644 --- a/浙江电压等级电量/test1.py +++ b/浙江电压等级电量/test1.py @@ -17,6 +17,7 @@ for city in df['地市'].drop_duplicates(): df_city['stat_date'] = df_city['stat_date'].map(lambda x:x.strip()) df_city['stat_date'] = pd.to_datetime(df_city['stat_date'],format='%Y-%m-%d') df_city = df_city[df_city.columns[:-1]] + df_city.sort_values(by='stat_date',ascending=True,inplace=True) df_city['stat_date'] = df_city['stat_date'].astype('str') df_city.to_excel(fr'C:\Users\user\Desktop\浙江各地市分电压日电量数据\{city}.xlsx',index=False) # file_Dir = r'C:\Users\鸽子\Desktop\浙江各地市行业电量数据' diff --git a/浙江电压等级电量/电压等级_输出为5.py b/浙江电压等级电量/电压等级_输出为5.py index 9f839fb..9ae6363 100644 --- a/浙江电压等级电量/电压等级_输出为5.py +++ b/浙江电压等级电量/电压等级_输出为5.py @@ -52,120 +52,119 @@ def data_preprocessing(data): return data # 拼接数据集 -file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据' -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)) - - - -# 训练 +# file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据' +# 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)) +# +# +# +# # 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) - -print(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,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()) - -# 保存模型 -torch.save(model.state_dict(),'dy5.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') -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 -plt.legend(loc='best') -plt.show() +# 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()) +# +# # 保存模型 +# torch.save(model.state_dict(),'dy5.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') +# 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 +# plt.legend(loc='best') +# plt.show() # 创建测试集 - -# max_value,min_value = 192751288.47,0.0 -# model.load_state_dict(torch.load('dy5.pth',map_location=torch.device('cpu'))) -# file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据' -# for excel in os.listdir(file_dir): -# df_city = pd.read_excel(os.path.join(file_dir,excel)) -# df_city.drop(columns=[i for i in df_city.columns if (df_city[i] == 0).sum() / len(df_city) >= 0.5], inplace=True) -# -# city = df_city['地市'].iloc[0] -# result_dict = {} -# for level in df_city.columns[2:]: -# x, y = create_dataset(df_city[level], 10) -# x = (x - min_value) / (max_value - min_value) -# x = x.reshape(-1, 1, 10) -# x = torch.from_numpy(x).type(torch.float32).to(device) -# pred = model(x).view(-1) -# pred = pred * (max_value - min_value) + min_value -# result = pred.detach().numpy()[-5:-2] -# result_dict[level] = list(result) -# df = pd.DataFrame(result_dict,index=['2023-10-29','2023-10-30','2023-10-31']) -# df.to_excel(fr'C:\Users\鸽子\Desktop\分压电量预测29-31\{city}.xlsx') -# print(result_dict) +max_value,min_value = 192751288.47,0.0 +model.load_state_dict(torch.load('dy5.pth',map_location=torch.device('cpu'))) +file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据' +for excel in os.listdir(file_dir): + df_city = pd.read_excel(os.path.join(file_dir,excel)) + df_city.drop(columns=[i for i in df_city.columns if (df_city[i] == 0).sum() / len(df_city) >= 0.5], inplace=True) + + city = df_city['地市'].iloc[0] + result_dict = {} + for level in df_city.columns[2:]: + x, y = create_dataset(df_city[level], 10) + x = (x - min_value) / (max_value - min_value) + x = x.reshape(-1, 1, 10) + x = torch.from_numpy(x).type(torch.float32).to(device) + pred = model(x).view(-1) + pred = pred * (max_value - min_value) + min_value + result = pred.cpu().detach().numpy()[-5:-2] + result_dict[level] = list(result) + df = pd.DataFrame(result_dict,index=['2023-10-29','2023-10-30','2023-10-31']) + df.to_excel(fr'C:\Users\user\Desktop\分压电量预测29-31\{city} .xlsx') +print(result_dict)