exp_main_F.py

from data_provider.data_factory import data_provider
from exp.exp_basic import Exp_Basic
from models import Informer, Autoformer, Transformer, DLinear, Linear, NLinear, SCINet, Film, FITS, Real_FITS
from utils.tools import EarlyStopping, adjust_learning_rate, visual, test_params_flop
from utils.metrics import metric

import numpy as np
import torch
import torch.nn as nn
from torch import optim
from utils.augmentations import augmentation
import os
import time

import warnings
import matplotlib.pyplot as plt
import numpy as np

from thop import profile

warnings.filterwarnings('ignore')

class Exp_Main(Exp_Basic):
    def __init__(self, args):
        super(Exp_Main, self).__init__(args)

    def _build_model(self):
        model_dict = {
            'Autoformer': Autoformer,
            'Transformer': Transformer,
            'Informer': Informer,
            'DLinear': DLinear,
            'NLinear': NLinear,
            'Linear': Linear,
            'SCINet': SCINet,
            'Film': Film,
            'FITS': FITS,
            'Real_FITS': Real_FITS
        }
        model = model_dict[self.args.model].Model(self.args).float()

        if self.args.use_multi_gpu and self.args.use_gpu:
            model = nn.DataParallel(model, device_ids=self.args.device_ids)
        
        return model

    def _get_data(self, flag):
        data_set, data_loader = data_provider(self.args, flag)
        return data_set, data_loader

    def _select_optimizer(self):
        model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
        print('!!!!!!!!!!!!!!learning rate!!!!!!!!!!!!!!!')
        print(self.args.learning_rate)
        return model_optim

    def _select_criterion(self):
        criterion = nn.MSELoss()
        return criterion

    def _get_profile(self, model):
        _input=torch.randn(self.args.batch_size, self.args.seq_len, self.args.enc_in).to(self.device)
        macs, params = profile(model, inputs=(_input,))
        print('FLOPs: ', macs)
        print('params: ', params)
        return macs, params

    def vali(self, vali_data, vali_loader, criterion):
        total_loss = []
        self.model.eval()
        with torch.no_grad():
            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(vali_loader):
                batch_x = batch_x.float().to(self.device)
                batch_y = batch_y.float().to(self.device)[:,-self.args.pred_len:,:]
                batch_xy = torch.cat([batch_x, batch_y], dim=1)

                batch_x_mark = batch_x_mark.float().to(self.device)
                batch_y_mark = batch_y_mark.float().to(self.device)

                # decoder input
                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
                # encoder - decoder
                if 'FITS' in self.args.model:
                    outputs, low = self.model(batch_x)
                elif 'SCINet' in self.args.model:
                    outputs = self.model(batch_x)
                else:
                    if self.args.output_attention:
                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
                    else:
                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
                f_dim = -1 if self.args.features == 'MS' else 0
                outputs = outputs[:, -self.args.pred_len:, f_dim:]
                batch_y = batch_y[:, -self.args.pred_len:, f_dim:]

                pred = outputs.detach().cpu()
                true = batch_y.detach().cpu()

                loss = criterion(pred, true)

                total_loss.append(loss)
        total_loss = np.average(total_loss)
        self.model.train()
        return total_loss

    def train(self, setting, ft=False):
        train_data, train_loader = self._get_data(flag='train')
        vali_data, vali_loader = self._get_data(flag='val')
        test_data, test_loader = self._get_data(flag='test')
        print(self.model)
        self._get_profile(self.model)
        print('Trainable parameters: ', sum(p.numel() for p in self.model.parameters() if p.requires_grad))

        path = os.path.join(self.args.checkpoints, setting)
        if not os.path.exists(path):
            os.makedirs(path)

        time_now = time.time()

        train_steps = len(train_loader)
        early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)

        model_optim = self._select_optimizer()
        criterion = self._select_criterion()

        for epoch in range(self.args.train_epochs):
            iter_count = 0
            train_loss = []

            self.model.train()
            epoch_time = time.time()
            if self.args.in_dataset_augmentation:
                train_loader.dataset.regenerate_augmentation_data()

            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(train_loader):
                iter_count += 1
                model_optim.zero_grad()

                batch_x = batch_x.float().to(self.device)
                batch_y = batch_y.float().to(self.device)[:,-self.args.pred_len:,:]
                batch_x_mark = batch_x_mark.float().to(self.device)
                batch_y_mark = batch_y_mark.float().to(self.device)
                # print(batch_x.shape, batch_y.shape)
                batch_xy = torch.cat([batch_x, batch_y], dim=1)

                # if self.args.in_batch_augmentation:
                #     aug = augmentation('batch')
                #     methods = {'f_mask':aug.freq_mask, 'f_mix': aug.freq_mix, 'noise':aug.noise,'noise_input':aug.noise_input}
                #     for step in range(self.args.aug_data_size):
                #         xy = methods[self.args.aug_method](batch_x, batch_y[:, -self.args.pred_len:, :], rate=self.args.aug_rate, dim=1)
                #         batch_x2, batch_y2 = xy[:, :self.args.seq_len, :], xy[:, -self.args.label_len-self.args.pred_len:, :]
                #         if 'noise' not in self.args.aug_method:
                #             batch_x = torch.cat([batch_x,batch_x2],dim=0)
                #             batch_y = torch.cat([batch_y,batch_y2],dim=0)
                #             batch_x_mark = torch.cat([batch_x_mark,batch_x_mark],dim=0)
                #             batch_y_mark = torch.cat([batch_y_mark,batch_y_mark],dim=0)
                #         else:
                #             print('noise')
                #             batch_x = batch_x2
                #             batch_y = batch_y2

                # decoder input
                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)

                # encoder - decoder
                if 'FITS' in self.args.model:
                        outputs, low = self.model(batch_x)
                elif 'SCINet' in self.args.model:
                        outputs = self.model(batch_x)
                else:
                    if self.args.output_attention:
                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
                    else:
                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark, batch_y)

                # print(outputs.shape,batch_y.shape)
                f_dim = -1 if self.args.features == 'MS' else 0
                if ft:
                    outputs = outputs[:, -self.args.pred_len:, f_dim:]
                    batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
                    # print(outputs.shape,batch_xy.shape)
                    #loss = criterion(outputs, batch_xy)
                    loss = criterion(outputs, batch_y)
                else: 
                    outputs = outputs[:, :, f_dim:]
                    # batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device) #???
                    loss = criterion(outputs, batch_xy)
                train_loss.append(loss.item())

                if (i + 1) % 100 == 0:
                    print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
                    speed = (time.time() - time_now) / iter_count
                    left_time = speed * ((self.args.train_epochs - epoch) * train_steps - i)
                    print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
                    iter_count = 0
                    time_now = time.time()

                loss.backward()
                model_optim.step()

            print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
            train_loss = np.average(train_loss)
            vali_loss = self.vali(vali_data, vali_loader, criterion)
            test_loss = self.vali(test_data, test_loader, criterion)

            print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f} Test Loss: {4:.7f}".format(
                epoch + 1, train_steps, train_loss, vali_loss, test_loss))
            early_stopping(vali_loss, self.model, path)
            if early_stopping.early_stop:
                print("Early stopping")
                break

            adjust_learning_rate(model_optim, epoch + 1, self.args)

        best_model_path = path + '/' + 'checkpoint.pth'
        self.model.load_state_dict(torch.load(best_model_path))

        return self.model

    def test(self, setting, test=0):
        test_data, test_loader = self._get_data(flag='test')
        
        if test:
            print('loading model')
            self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))

        preds = []
        trues = []
        inputx = []
        reconx = []
        inputxy = []
        reconxy = []
        lows = []
        folder_path = './test_results/' + setting + '/'
        if not os.path.exists(folder_path):
            os.makedirs(folder_path)

        self.model.eval()
        with torch.no_grad():
            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(test_loader):
                batch_x = batch_x.float().to(self.device)
                batch_y = batch_y.float().to(self.device)[:,-self.args.pred_len:,:]
                batch_xy = torch.cat([batch_x, batch_y], dim=1).float().to(self.device)

                batch_x_mark = batch_x_mark.float().to(self.device)
                batch_y_mark = batch_y_mark.float().to(self.device)

                # decoder input
                dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
                # encoder - decoder
                
                if 'FITS' in self.args.model:
                        outputs, low = self.model(batch_x)
                elif 'SCINet' in self.args.model:
                        outputs = self.model(batch_x)
                else:
                    if self.args.output_attention:
                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]

                    else:
                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)

                f_dim = -1 if self.args.features == 'MS' else 0
                # print(outputs.shape,batch_y.shape)
                outputs_ = outputs[:, -self.args.pred_len:, f_dim:]
                batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
                outputs_ = outputs_.detach().cpu().numpy()
                batch_y = batch_y.detach().cpu().numpy()


                pred = outputs_  # outputs.detach().cpu().numpy()  # .squeeze()
                true = batch_y  # batch_y.detach().cpu().numpy()  # .squeeze()

                preds.append(pred)
                trues.append(true)
                inputx.append(batch_x.detach().cpu().numpy())
                inputxy.append(batch_xy.detach().cpu().numpy())
                reconx.append(outputs[:, :-self.args.pred_len, f_dim:].detach().cpu().numpy())
                reconxy.append(outputs.detach().cpu().numpy())
                lows.append(low.detach().cpu().numpy())
                if i % 20 == 0:
                    input = batch_x.detach().cpu().numpy()
                    gt = np.concatenate((input[0, :, -1], true[0, :, -1]), axis=0)
                    pd = np.concatenate((input[0, :, -1], pred[0, :, -1]), axis=0)
                    visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf'))

        if self.args.test_flop:
            test_params_flop((batch_x.shape[1],batch_x.shape[2]))
            exit()
        preds = np.concatenate(preds, axis=0)
        trues = np.concatenate(trues, axis=0)
        # inputx = np.array(inputx)
        # reconx = np.array(reconx)
        # reconxy = np.array(reconxy)
        # inputxy = np.array(inputxy)
        # lows = np.array(lows)


        # preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
        # trues = trues.reshape(-1, trues.shape[-2], trues.shape[-1])
        # inputx = inputx.reshape(-1, inputx.shape[-2], inputx.shape[-1])
        # reconx = reconx.reshape(-1, reconx.shape[-2], reconx.shape[-1])
        # reconxy = reconxy.reshape(-1, reconxy.shape[-2], reconxy.shape[-1])
        # inputxy = inputxy.reshape(-1, inputxy.shape[-2], inputxy.shape[-1])
        # lows = lows.reshape(-1, lows.shape[-2], lows.shape[-1])

        # try: 
        #     for i in range(0,2800,300):
                
        #         # create a figure with 3 subplots
        #         fig, axs = plt.subplots(3, 1, figsize=(10, 10))
        #         # plot pred and true in the first subplot
        #         axs[0].plot(trues[i, :, -1], label='true')
        #         axs[0].plot(preds[i, :, -1], label='pred')
        #         axs[0].set_title('pred and true')
        #         # plot inputx and reconx in the second subplot
        #         axs[1].plot(inputx[i, :, -1], label='inputx')
        #         axs[1].plot(reconx[i, :, -1], label='reconx')
        #         axs[1].set_title('inputx and reconx')
        #         # plot inputxy and reconxy in the third subplot
        #         axs[2].plot(inputxy[i, :, -1], label='inputxy')
        #         axs[2].plot(reconxy[i, :, -1], label='reconxy')
        #         axs[2].plot(lows[i, :, -1])
        #         axs[2].set_title('inputxy and reconxy')
        #         # show the legend
        #         plt.legend()
        #         # save the figure to file
        #         fig.savefig(os.path.join(folder_path, str(i) + '_F.png'))
        #         # print('plottting')
        # except:
        #     pass

        # result save
        folder_path = './results/' + setting + '/'
        if not os.path.exists(folder_path):
            os.makedirs(folder_path)

        mae, mse, rmse, mape, mspe, rse, corr = metric(preds, trues)
        print('mse:{}, mae:{}, rse:{}, corr:{}'.format(mse, mae, rse, corr))
        f = open("result.txt", 'a')
        f.write(setting + "  \n")
        f.write('mse:{}, mae:{}, rse:{}, corr:{}'.format(mse, mae, rse, corr))
        f.write('\n')
        f.write('\n')
        f.close()

        # np.save(folder_path + 'metrics.npy', np.array([mae, mse, rmse, mape, mspe,rse, corr]))
        np.save(folder_path + 'pred.npy', preds)
        np.save(folder_path + 'true.npy', trues)
        np.save(folder_path + 'x.npy', inputx)
        return

    def predict(self, setting, load=False):
        pred_data, pred_loader = self._get_data(flag='pred')

        if load:
            path = os.path.join(self.args.checkpoints, setting)
            best_model_path = path + '/' + 'checkpoint.pth'
            self.model.load_state_dict(torch.load(best_model_path))

        preds = []

        self.model.eval()
        with torch.no_grad():
            for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(pred_loader):
                batch_x = batch_x.float().to(self.device)
                batch_y = batch_y.float()
                batch_x_mark = batch_x_mark.float().to(self.device)
                batch_y_mark = batch_y_mark.float().to(self.device)

                # decoder input
                dec_inp = torch.zeros([batch_y.shape[0], self.args.pred_len, batch_y.shape[2]]).float().to(batch_y.device)
                dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
                # encoder - decoder
                if 'Linear' in self.args.model:
                    outputs = self.model(batch_x)
                else:
                    if self.args.output_attention:
                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
                    else:
                        outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
                pred = outputs.detach().cpu().numpy()  # .squeeze()
                preds.append(pred)

        preds = np.array(preds)
        preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])

        # result save
        folder_path = './results/' + setting + '/'
        if not os.path.exists(folder_path):
            os.makedirs(folder_path)

        np.save(folder_path + 'real_prediction.npy', preds)

        return