| import torch | |
| import copy | |
| import pandas as pd | |
| import logging | |
| from tqdm import trange, tqdm | |
| from sklearn.metrics import confusion_matrix, accuracy_score, f1_score | |
| from sklearn.neighbors import LocalOutlierFactor | |
| from utils.functions import restore_model, save_model | |
| from utils.metrics import F_measure | |
| from .KNNCL_utils import create_negative_dataset, generate_positive_sample, _prepare_inputs | |
| class KNNCLManager: | |
| def __init__(self, args, data, model, logger_name = 'Detection'): | |
| self.logger = logging.getLogger(logger_name) | |
| self.set_model_optimizer(args, data, model) | |
| self.data = data | |
| self.train_dataloader = data.dataloader.train_labeled_loader | |
| self.eval_dataloader= data.dataloader.eval_loader | |
| self.test_dataloader = data.dataloader.test_loader | |
| self.negative_data = create_negative_dataset(self.train_dataloader) | |
| if not args.train: | |
| restore_model(self.model, args.model_output_dir) | |
| def set_model_optimizer(self, args, data, model): | |
| self.model = model.set_model(args, 'bert') | |
| self.optimizer, self.scheduler = model.set_optimizer(self.model, data.dataloader.num_train_examples, args.train_batch_size, \ | |
| args.num_train_epochs, args.lr, args.warmup_proportion) | |
| self.device = model.device | |
| def train(self, args, data): | |
| best_model = None | |
| best_eval_score = 0 | |
| wait = 0 | |
| for epoch in trange(int(args.num_train_epochs), desc="Epoch"): | |
| self.model.train() | |
| tr_loss = 0 | |
| nb_tr_examples, nb_tr_steps = 0, 0 | |
| for step, batch in enumerate(tqdm(self.train_dataloader, desc="Iteration")): | |
| batch = tuple(t.to(self.device) for t in batch) | |
| input_ids, input_mask, segment_ids, label_ids = batch | |
| positive_sample = None | |
| positive_sample = generate_positive_sample(self.negative_data,args, label_ids) | |
| positive_sample = _prepare_inputs(self.device, positive_sample) | |
| batch_dict = {"labels":label_ids,"input_ids":input_ids,"token_type_ids":segment_ids,"attention_mask":input_mask} | |
| outputs = self.model(batch_dict, mode='train', positive_sample=positive_sample) | |
| loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0] | |
| self.optimizer.zero_grad() | |
| loss.backward() | |
| torch.nn.utils.clip_grad_norm_(self.model.parameters(), args.clip) | |
| self.optimizer.step() | |
| self.scheduler.step() | |
| tr_loss += loss.item() | |
| nb_tr_examples += input_ids.size(0) | |
| nb_tr_steps += 1 | |
| loss = tr_loss / nb_tr_steps | |
| y_true, y_pred = self.get_outputs(args, data, mode='eval') | |
| eval_score = round(f1_score(y_true, y_pred, average='macro') * 100, 2) | |
| eval_results = { | |
| 'train_loss': loss, | |
| 'eval_score': eval_score, | |
| 'best_eval_score': best_eval_score | |
| } | |
| self.logger.info("***** Epoch: %s: Eval results *****", str(epoch + 1)) | |
| for key in sorted(eval_results.keys()): | |
| self.logger.info(" %s = %s", key, str(eval_results[key])) | |
| if eval_score >= best_eval_score: | |
| best_model = copy.deepcopy(self.model) | |
| wait = 0 | |
| best_eval_score = eval_score | |
| else: | |
| wait += 1 | |
| if wait >= args.wait_patient: | |
| break | |
| self.model = best_model | |
| if args.save_model: | |
| save_model(self.model, args.model_output_dir) | |
| def classify_lof(self, args, data, preds, train_feats, pred_feats): | |
| lof = LocalOutlierFactor(n_neighbors=args.n_neighbors, contamination = args.contamination, novelty=True, n_jobs=-1) | |
| lof.fit(train_feats) | |
| y_pred_lof = pd.Series(lof.predict(pred_feats)) | |
| preds[y_pred_lof[y_pred_lof == -1].index] = data.unseen_label_id | |
| return preds | |
| def get_outputs(self, args, data, mode, get_feats = False, train_feats = None): | |
| if mode == 'train': | |
| dataloader = self.train_dataloader | |
| elif mode == 'eval': | |
| dataloader = self.eval_dataloader | |
| elif mode == 'test': | |
| dataloader = self.test_dataloader | |
| self.model.eval() | |
| total_labels = torch.empty(0,dtype=torch.long).to(self.device) | |
| total_probs = torch.empty((0, data.num_labels)).to(self.device) | |
| total_features = torch.empty((0,args.feat_dim)).to(self.device) | |
| for batch in tqdm(dataloader, desc="Iteration"): | |
| batch = tuple(t.to(self.device) for t in batch) | |
| input_ids, input_mask, segment_ids, label_ids = batch | |
| batch_dict = {"labels":label_ids,"input_ids":input_ids,"token_type_ids":segment_ids,"attention_mask":input_mask} | |
| with torch.set_grad_enabled(False): | |
| output = self.model(batch_dict, mode='test') | |
| total_labels = torch.cat((total_labels,label_ids)) | |
| total_probs = torch.cat((total_probs, output[0])) | |
| total_features = torch.cat((total_features, output[1])) | |
| if get_feats: | |
| feats = total_features.cpu().numpy() | |
| return feats | |
| else: | |
| total_probs, y_pred = total_probs.max(dim = 1) | |
| y_pred = y_pred.cpu().numpy() | |
| y_true = total_labels.cpu().numpy() | |
| if train_feats is not None: | |
| feats = total_features.cpu().numpy() | |
| y_pred = self.classify_lof(args, data, y_pred, train_feats, feats) | |
| return y_true, y_pred | |
| def test(self, args, data, show=False): | |
| train_feats = self.get_outputs(args, data, mode = 'train', get_feats = True) | |
| y_true, y_pred = self.get_outputs(args, data, mode = 'test', train_feats = train_feats) | |
| cm = confusion_matrix(y_true, y_pred) | |
| test_results = F_measure(cm) | |
| acc = round(accuracy_score(y_true, y_pred) * 100, 2) | |
| test_results['Acc'] = acc | |
| self.logger.info | |
| self.logger.info("***** Test: Confusion Matrix *****") | |
| self.logger.info("%s", str(cm)) | |
| self.logger.info("***** Test results *****") | |
| for key in sorted(test_results.keys()): | |
| self.logger.info(" %s = %s", key, str(test_results[key])) | |
| test_results['y_true'] = y_true | |
| test_results['y_pred'] = y_pred | |
| return test_results | |