193 lines
7.3 KiB
Python
193 lines
7.3 KiB
Python
from data_provider.data_factory import data_provider
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from exp.exp_basic import Exp_Basic
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from utils.tools import EarlyStopping, adjust_learning_rate, cal_accuracy
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import torch
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import torch.nn as nn
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from torch import optim
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import os
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import time
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import warnings
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import numpy as np
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import pdb
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warnings.filterwarnings('ignore')
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class Exp_Classification(Exp_Basic):
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def __init__(self, args):
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super(Exp_Classification, self).__init__(args)
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def _build_model(self):
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# model input depends on data
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train_data, train_loader = self._get_data(flag='TRAIN')
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test_data, test_loader = self._get_data(flag='TEST')
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self.args.seq_len = max(train_data.max_seq_len, test_data.max_seq_len)
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self.args.pred_len = 96
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self.args.enc_in = train_data.feature_df.shape[1]
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self.args.num_class = len(train_data.class_names)
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# model init
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model = self.model_dict[self.args.model].Model(self.args).float()
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if self.args.use_multi_gpu and self.args.use_gpu:
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model = nn.DataParallel(model, device_ids=self.args.device_ids)
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return model
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def _get_data(self, flag):
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data_set, data_loader = data_provider(self.args, flag)
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return data_set, data_loader
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def _select_optimizer(self):
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# model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
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model_optim = optim.RAdam(self.model.parameters(), lr=self.args.learning_rate)
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return model_optim
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def _select_criterion(self):
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criterion = nn.CrossEntropyLoss()
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return criterion
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def vali(self, vali_data, vali_loader, criterion):
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total_loss = []
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preds = []
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trues = []
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self.model.eval()
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with torch.no_grad():
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for i, (batch_x, label, padding_mask) in enumerate(vali_loader):
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batch_x = batch_x.float().to(self.device)
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padding_mask = padding_mask.float().to(self.device)
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label = label.to(self.device)
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outputs = self.model(batch_x, padding_mask, None, None)
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pred = outputs.detach()
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loss = criterion(pred, label.long().squeeze())
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total_loss.append(loss.item())
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preds.append(outputs.detach())
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trues.append(label)
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total_loss = np.average(total_loss)
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preds = torch.cat(preds, 0)
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trues = torch.cat(trues, 0)
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probs = torch.nn.functional.softmax(preds) # (total_samples, num_classes) est. prob. for each class and sample
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predictions = torch.argmax(probs, dim=1).cpu().numpy() # (total_samples,) int class index for each sample
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trues = trues.flatten().cpu().numpy()
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accuracy = cal_accuracy(predictions, trues)
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self.model.train()
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return total_loss, accuracy
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def train(self, setting):
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train_data, train_loader = self._get_data(flag='TRAIN')
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vali_data, vali_loader = self._get_data(flag='TEST')
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test_data, test_loader = self._get_data(flag='TEST')
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path = os.path.join(self.args.checkpoints, setting)
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if not os.path.exists(path):
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os.makedirs(path)
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time_now = time.time()
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train_steps = len(train_loader)
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early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)
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model_optim = self._select_optimizer()
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criterion = self._select_criterion()
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for epoch in range(self.args.train_epochs):
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iter_count = 0
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train_loss = []
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self.model.train()
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epoch_time = time.time()
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for i, (batch_x, label, padding_mask) in enumerate(train_loader):
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iter_count += 1
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model_optim.zero_grad()
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batch_x = batch_x.float().to(self.device)
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padding_mask = padding_mask.float().to(self.device)
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label = label.to(self.device)
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outputs = self.model(batch_x, padding_mask, None, None)
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loss = criterion(outputs, label.long().squeeze(-1))
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train_loss.append(loss.item())
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if (i + 1) % 100 == 0:
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print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
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speed = (time.time() - time_now) / iter_count
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left_time = speed * ((self.args.train_epochs - epoch) * train_steps - i)
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print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
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iter_count = 0
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time_now = time.time()
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loss.backward()
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nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=4.0)
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model_optim.step()
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print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
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train_loss = np.average(train_loss)
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vali_loss, val_accuracy = self.vali(vali_data, vali_loader, criterion)
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# test_loss, test_accuracy = self.vali(test_data, test_loader, criterion)
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print(
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"Epoch: {0}, Steps: {1} | Train Loss: {2:.3f} Vali Loss: {3:.3f} Vali Acc: {4:.3f}" # Test Loss: {5:.3f} Test Acc: {6:.3f}"
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.format(epoch + 1, train_steps, train_loss, vali_loss, val_accuracy))
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# test_loss, test_accuracy))
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early_stopping(-val_accuracy, self.model, path)
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if early_stopping.early_stop:
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print("Early stopping")
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break
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best_model_path = path + '/' + 'checkpoint.pth'
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self.model.load_state_dict(torch.load(best_model_path))
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return self.model
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def test(self, setting, test=0):
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test_data, test_loader = self._get_data(flag='TEST')
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if test:
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print('loading model')
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self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))
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preds = []
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trues = []
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folder_path = './test_results/' + setting + '/'
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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self.model.eval()
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with torch.no_grad():
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for i, (batch_x, label, padding_mask) in enumerate(test_loader):
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batch_x = batch_x.float().to(self.device)
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padding_mask = padding_mask.float().to(self.device)
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label = label.to(self.device)
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outputs = self.model(batch_x, padding_mask, None, None)
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preds.append(outputs.detach())
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trues.append(label)
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preds = torch.cat(preds, 0)
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trues = torch.cat(trues, 0)
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print('test shape:', preds.shape, trues.shape)
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probs = torch.nn.functional.softmax(preds) # (total_samples, num_classes) est. prob. for each class and sample
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predictions = torch.argmax(probs, dim=1).cpu().numpy() # (total_samples,) int class index for each sample
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trues = trues.flatten().cpu().numpy()
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accuracy = cal_accuracy(predictions, trues)
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# result save
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folder_path = './results/' + setting + '/'
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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print('accuracy:{}'.format(accuracy))
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file_name='result_classification.txt'
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f = open(os.path.join(folder_path,file_name), 'a')
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f.write(setting + " \n")
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f.write('accuracy:{}'.format(accuracy))
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f.write('\n')
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f.write('\n')
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f.close()
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return
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