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main_informer_local.py

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+import argparse
+import os
+import torch
+
+from exp.exp_informer import Exp_Informer
+
+parser = argparse.ArgumentParser(description='[Informer] Long Sequences Forecasting')
+
+parser.add_argument('--model', type=str, required=False, default='informer',help='model of experiment, options: [informer, informerstack, informerlight(TBD)]')
+
+parser.add_argument('--data', type=str, required=False, default='SH000001.csv', help='data')
+# parser.add_argument('--data', type=str, required=True, default='ETTh1', help='data')
+parser.add_argument('--root_path', type=str, default='./data/stock/', help='root path of the data file')
+parser.add_argument('--data_path', type=str, default='SH000001.csv', help='data file')
+# parser.add_argument('--root_path', type=str, default='./data/ETT/', help='root path of the data file')
+# parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
+parser.add_argument('--features', type=str, default='M', help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
+parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
+parser.add_argument('--freq', type=str, default='h', help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')
+parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
+
+parser.add_argument('--seq_len', type=int, default=96, help='input sequence length of Informer encoder')
+parser.add_argument('--label_len', type=int, default=48, help='start token length of Informer decoder')
+parser.add_argument('--pred_len', type=int, default=24, help='prediction sequence length')
+# Informer decoder input: concat[start token series(label_len), zero padding series(pred_len)]
+
+parser.add_argument('--enc_in', type=int, default=7, help='encoder input size')
+parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
+parser.add_argument('--c_out', type=int, default=7, help='output size')
+parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
+parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
+parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
+parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
+parser.add_argument('--s_layers', type=str, default='3,2,1', help='num of stack encoder layers')
+parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
+parser.add_argument('--factor', type=int, default=5, help='probsparse attn factor')
+parser.add_argument('--padding', type=int, default=0, help='padding type')
+parser.add_argument('--distil', action='store_false', help='whether to use distilling in encoder, using this argument means not using distilling', default=True)
+parser.add_argument('--dropout', type=float, default=0.05, help='dropout')
+parser.add_argument('--attn', type=str, default='prob', help='attention used in encoder, options:[prob, full]')
+parser.add_argument('--embed', type=str, default='timeF', help='time features encoding, options:[timeF, fixed, learned]')
+parser.add_argument('--activation', type=str, default='gelu',help='activation')
+parser.add_argument('--output_attention', action='store_true', help='whether to output attention in ecoder')
+parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')
+parser.add_argument('--mix', action='store_false', help='use mix attention in generative decoder', default=True)
+parser.add_argument('--cols', type=str, nargs='+', help='certain cols from the data files as the input features')
+parser.add_argument('--num_workers', type=int, default=0, help='data loader num workers')
+parser.add_argument('--itr', type=int, default=2, help='experiments times')
+parser.add_argument('--train_epochs', type=int, default=6, help='train epochs')
+parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
+parser.add_argument('--patience', type=int, default=3, help='early stopping patience')
+parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
+parser.add_argument('--des', type=str, default='test',help='exp description')
+parser.add_argument('--loss', type=str, default='mse',help='loss function')
+parser.add_argument('--lradj', type=str, default='type1',help='adjust learning rate')
+parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False)
+parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)
+
+parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu')
+parser.add_argument('--gpu', type=int, default=0, help='gpu')
+parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=False)
+parser.add_argument('--devices', type=str, default='0,1,2,3',help='device ids of multile gpus')
+
+args = parser.parse_args()
+
+args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False
+
+def denormalize(normalized_num, min_val, max_val):
+    """
+    还原函数，将归一化后的数值还原为原始数值
+    """
+    return normalized_num * (max_val - min_val) + min_val
+
+if args.use_gpu and args.use_multi_gpu:
+    args.devices = args.devices.replace(' ','')
+    device_ids = args.devices.split(',')
+    args.device_ids = [int(id_) for id_ in device_ids]
+    args.gpu = args.device_ids[0]
+
+args.model = 'informer'  # model of experiment, options: [informer, informerstack, informerlight(TBD)]
+args.data = 'custom'  # data
+args.root_path = './data/stock/'  # root path of data file
+args.data_path = 'SH600000.csv'  # data file
+# args.data_path = 'SH000001.csv'  # data file
+args.features = 'MS'  # forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate
+args.target = 'Close'  # target feature in S or MS task
+args.freq = 'd'  # freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h
+args.checkpoints = './checkpoints'  # location of model checkpoints
+
+args.seq_len = 20  # input sequence length of Informer encoder
+args.label_len = 10  # start token length of Informer decoder
+args.pred_len = 5  # prediction sequence length
+# Informer decoder input: concat[start token series(label_len), zero padding series(pred_len)]
+
+args.enc_in = 5  # encoder input size
+args.dec_in = 5  # decoder input size
+args.c_out = 1  # output size
+args.factor = 5  # probsparse attn factor
+args.padding = 0  # padding type
+args.d_model = 256  # dimension of model
+args.n_heads = 4  # num of heads
+args.e_layers = 2  # num of encoder layers
+args.d_layers = 1  # num of decoder layers
+args.d_ff = 256  # dimension of fcn in model
+args.dropout = 0.05  # dropout
+args.attn = 'prob'  # attention used in encoder, options:[prob, full]
+args.embed = 'timeF'  # time features encoding, options:[timeF, fixed, learned]
+args.activation = 'gelu'  # activation
+args.distil = True  # whether to use distilling in encoder
+args.output_attention = False  # whether to output attention in ecoder
+
+args.batch_size = 32
+args.learning_rate = 0.0001
+args.loss = 'mse'
+args.lradj = 'type1'
+args.use_amp = False  # whether to use automatic mixed precision training
+
+args.num_workers = 0
+args.train_epochs = 20
+args.patience = 3
+args.des = 'exp'
+
+# args.use_gpu = True if torch.cuda.is_available() else False
+args.use_gpu = False
+args.gpu = 0
+
+args.use_multi_gpu = False
+args.devices = '0,1,2,3'
+
+args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False
+
+if args.use_gpu and args.use_multi_gpu:
+    args.devices = args.devices.replace(' ','')
+    device_ids = args.devices.split(',')
+    args.device_ids = [int(id_) for id_ in device_ids]
+    args.gpu = args.device_ids[0]
+
+args.detail_freq = args.freq
+args.freq = args.freq[-1:]
+#%%
+print('Args in experiment:')
+print(args)
+
+
+Exp = Exp_Informer
+#
+# for ii in range(args.itr):
+#     # setting record of experiments
+#     setting = ('{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_at{}_fc{}_eb{}_dt{}_mx{}_{}_{}'.
+#                format(args.model, args.data, args.features,
+#                 args.seq_len, args.label_len, args.pred_len,
+#                 args.d_model, args.n_heads, args.e_layers, args.d_layers, args.d_ff, args.attn, args.factor,
+#                 args.embed, args.distil, args.mix, args.des, ii))
+#
+#     exp = Exp(args) # set experiments
+#     print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
+#     exp.train(setting)
+#
+#     print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
+#     exp.test(setting)
+#
+#     if args.do_predict:
+#         print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
+#         exp.predict(setting, True)
+#
+#     torch.cuda.empty_cache()
+#
+#
+# # the prediction will be saved in ./results1/{setting}/real_prediction.npy
+
+import pandas as pd
+
+# 读取CSV文件
+csv_file = './data/stock/SH000001.csv'
+# csv_file = './data/stock/SH600000.csv'
+data = pd.read_csv(csv_file)
+
+# 找到最大值和最小值
+max_value = data['High'].max()
+min_value = data['Low'].min()
+last_dat = data['date'].iloc[-1]
+from datetime import datetime, timedelta
+date_obj = datetime.strptime(last_dat, '%Y/%m/%d')
+# 创建一个空列表来存储后五天的日期
+next_dates = []
+
+# 计算后五天的日期并将其添加到列表中
+for i in range(5):
+    next_date = date_obj + timedelta(days=i + 1)
+    next_dates.append(next_date.strftime('%Y/%m/%d'))
+# print("最大值：", max_value)
+# print("最小值：", min_value)
+
+def denormalize(normalized_num, min_val, max_val):
+    """
+    还原函数，将归一化后的数值还原为原始数值
+    """
+    return normalized_num * (max_val - min_val) + min_val
+
+import numpy as np
+import matplotlib.pyplot as plt
+setting = 'informer_custom_ftMS_sl20_ll10_pl5_dm256_nh4_el2_dl1_df256_atprob_fc5_ebtimeF_dtTrue_exp'
+# setting = 'informer_custom_ftMS_sl20_ll10_pl5_dm256_nh4_el2_dl1_df256_atprob_fc5_ebtimeF_dtTrue_mxTrue_exp_0'
+prediction = np.load('./results1/' + setting + '/real_prediction.npy')
+
+print(prediction.shape)
+
+
+
+# plt.figure()
+prediction[0, :, -1]= denormalize(prediction[0, :, -1], min_value, max_value)
+x = [1, 2, 3, 4, 5]
+plt.plot(next_dates,prediction[0, :, -1])
+plt.xlabel("day")
+plt.ylabel("price")
+plt.title("Price trend in the next 5 days")
+# # 创建示例数据
+
+# # 设置x轴和y轴的刻度起始值为1
+# plt.xticks(range(1, max(x) + 1))
+
+plt.show()
+
+# preds = np.load('./results1/'+setting+'/pred.npy')
+# trues = np.load('./results1/'+setting+'/true.npy')
+
+# [samples, pred_len, dimensions]
+# plt.figure()
+# plt.plot(trues[10,:,-1], label='GroundTruth')
+# plt.plot(preds[10,:,-1], label='Prediction')
+# plt.legend()
+# plt.show()
+#
+#
+# plt.figure()
+# plt.plot(trues[20,:,-1], label='GroundTruth')
+# plt.plot(preds[20,:,-1], label='Prediction')
+# plt.legend()
+# plt.show()
+#
+#
+# plt.figure()
+# plt.plot(trues[30,:,-1], label='GroundTruth')
+# plt.plot(preds[30,:,-1], label='Prediction')
+# plt.legend()
+# plt.show()
+
+# plt.figure()
+# plt.plot(trues[:,0,-1].reshape(-1), label='GroundTruth')
+# plt.plot(preds[:,0,-1].reshape(-1), label='Prediction')
+# plt.legend()
+# plt.show()