exp_main.py

from data_provider.data_factory import data_provider
from exp.exp_basic import Exp_Basic
from models import DLinear, NLinear, FreTS
from utils.tools import EarlyStopping, adjust_learning_rate, visual, test_params_flop
from utils.metrics import metric

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch import optim

import os
import time

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

import neptune

warnings.filterwarnings("ignore")


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

            self.neptune_run = neptune.init_run(
                project=args.neptune_run,
                api_token=args.neptune_key,
            )
            params = {**vars(self.args)}
            self.neptune_run["parameters/configuration/model/type"] = args.model
            self.neptune_run["parameters/configuration/model"] = params
            self.neptune_run["parameters/problem_spec"] = args.data
        else:
            self.neptune_run = None

    def _build_model(self):
        model_dict = {"DLinear": DLinear, "NLinear": NLinear, "FreLinear": FreTS}
        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)
        return model_optim

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

    def vali(self, vali_data, vali_loader, criterion, criterionmae=None):
        total_loss = []
        total_loss_mae = []
        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()
                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 self.args.use_amp:
                    with torch.cuda.amp.autocast():
                        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
                                )
                else:
                    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
                            )
                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:].to(self.device)

                pred = outputs.detach().cpu()
                true = batch_y.detach().cpu()
                for j in range(pred.shape[0]):
                    loss = criterion(pred[j], true[j]).item()
                    if criterionmae:
                        lossmae = criterionmae(pred[j], true[j]).item()
                        total_loss_mae.append(lossmae)
                    total_loss.append(loss)

        total_loss = np.average(total_loss)
        if total_loss_mae:
            total_loss_mae = np.average(total_loss_mae)
        self.model.train()
        return total_loss, total_loss_mae

    def train(self, setting):
        train_data, train_loader = self._get_data(flag="train")
        if not self.args.train_only:
            vali_data, vali_loader = self._get_data(flag="val")
            test_data, test_loader = self._get_data(flag="test")

        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()

        criterionmae = nn.L1Loss()

        total_params = 0
        for name, parameter in self.model.named_parameters():
            if not parameter.requires_grad:
                continue
            param = parameter.numel()
            total_params += param
        print(f"Total Trainable Params: {total_params}")
        if self.args.neptune_run:
            self.neptune_run["parameters/number_of_parameters"] = total_params

        if self.args.use_amp:
            scaler = torch.cuda.amp.GradScaler()

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

            self.model.train()
            epoch_time = time.time()
            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)

                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 self.args.use_amp:
                    with torch.cuda.amp.autocast():
                        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
                                )

                        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:].to(
                            self.device
                        )
                        loss = criterion(outputs, batch_y)
                        train_loss.append(loss.item())
                else:
                    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, batch_y
                            )
                    # print(outputs.shape,batch_y.shape)
                    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:].to(
                        self.device
                    )
                    loss = criterion(outputs, batch_y)
                    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()

                if self.args.use_amp:
                    scaler.scale(loss).backward()
                    scaler.step(model_optim)
                    scaler.update()
                else:
                    loss.backward()
                    model_optim.step()

            print(
                "Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time)
            )
            train_loss = np.average(train_loss)
            if not self.args.train_only:
                vali_loss, vali_loss_mae = self.vali(
                    vali_data, vali_loader, criterion, criterionmae
                )
                test_loss, _ = self.vali(test_data, test_loader, criterion)

                if self.neptune_run:
                    self.neptune_run["test_hard/mse_far"].append(vali_loss)
                    self.neptune_run["test_hard/mae_far"].append(vali_loss_mae)
                    self.neptune_run["train_hard/mse_far"].append(train_loss)
                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)
            else:
                print(
                    "Epoch: {0}, Steps: {1} | Train Loss: {2:.7f}".format(
                        epoch + 1, train_steps, train_loss
                    )
                )
                early_stopping(train_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 = []
        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)

                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 self.args.use_amp:
                    with torch.cuda.amp.autocast():
                        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
                                )
                else:
                    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
                            )

                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())
                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.array(preds)
        trues = np.array(trues)
        inputx = np.array(inputx)

        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])

        # 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:{}, rmse:{}".format(mse, mae, rmse))
        f = open("result.txt", "a")
        f.write(setting + "  \n")
        f.write("mse:{}, mae:{}, rse:{}, rmse:{}".format(mse, mae, rse, rmse))
        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 self.args.use_amp:
                    with torch.cuda.amp.autocast():
                        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
                                )
                else:
                    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])
        if pred_data.scale:
            preds = pred_data.inverse_transform(preds)

        # 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)
        pd.DataFrame(
            np.append(np.transpose([pred_data.future_dates]), preds[0], axis=1),
            columns=pred_data.cols,
        ).to_csv(folder_path + "real_prediction.csv", index=False)

        return