| from importlib import import_module |
| import torch |
| import numpy as np |
| import os |
| import copy |
| import logging |
| from torch import nn |
| from datetime import datetime |
| from sklearn.metrics import confusion_matrix, accuracy_score |
| from tqdm import trange, tqdm |
| from scipy.stats import norm as dist_model |
| from losses import loss_map |
| from utils.functions import restore_model, save_model |
| from utils.metrics import F_measure |
|
|
| class DOCManager: |
| |
| 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.loss_fct = loss_map[args.loss_fct] |
| |
| if args.train: |
| self.best_mu_stds = None |
|
|
| else: |
| 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 |
| wait = 0 |
| best_eval_score = 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 |
| with torch.set_grad_enabled(True): |
| |
| loss = self.model(input_ids, segment_ids, input_mask, label_ids, mode='train', loss_fct=self.loss_fct) |
|
|
| self.optimizer.zero_grad() |
| loss.backward() |
| 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(accuracy_score(y_true, y_pred) * 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 |
| |
| elif eval_score > 0: |
| wait += 1 |
| if wait >= args.wait_patient: |
| break |
|
|
| self.model = best_model |
|
|
| if args.save_model: |
| save_model(self.model, args.model_output_dir) |
| np.save(os.path.join(args.method_output_dir, 'mu_stds.npy'), self.best_mu_stds) |
|
|
| def test(self, args, data, show=False): |
| |
| mu_stds = self.get_outputs(args, data, mode = 'train', get_mu_stds = True) |
| y_true, y_pred = self.get_outputs(args, data, mode = 'test', mu_stds = mu_stds) |
|
|
| 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 |
|
|
| def get_outputs(self, args, data, mode = 'eval', get_feats = False, get_mu_stds = False, mu_stds = 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_logits = 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 |
| with torch.set_grad_enabled(False): |
|
|
| pooled_output, logits = self.model(input_ids, segment_ids, input_mask) |
|
|
| total_labels = torch.cat((total_labels,label_ids)) |
| total_logits = torch.cat((total_logits, logits)) |
| total_features = torch.cat((total_features, pooled_output)) |
|
|
| if get_feats: |
| |
| feats = total_features.cpu().numpy() |
| return feats |
|
|
| else: |
| |
| total_probs, y_pred = total_logits.max(dim = 1) |
| y_true = total_labels.cpu().numpy() |
| y_logit = total_logits.cpu().numpy() |
| y_pred = y_pred.cpu().numpy() |
| |
| if mode == 'eval': |
| return y_true, y_pred |
|
|
| else: |
| if get_mu_stds == True: |
| mu_stds = self.cal_mu_std(y_logit, y_true, data.num_labels) |
| return mu_stds |
| else: |
| y_pred = self.classify_doc(data, args, y_logit, mu_stds) |
|
|
| return y_true, y_pred |
|
|
| def classify_doc(self, data, args, y_prob, mu_stds): |
|
|
| thresholds = {} |
| for col in range(data.num_labels): |
| threshold = max(0.5, 1 - args.scale * mu_stds[col][1]) |
| label = data.known_label_list[col] |
| thresholds[label] = threshold |
| thresholds = np.array(thresholds) |
| self.logger.info('Probability thresholds of each class: %s', thresholds) |
| |
| y_pred = [] |
| for p in y_prob: |
| max_class = np.argmax(p) |
| max_value = np.max(p) |
| threshold = max(0.5, 1 - args.scale * mu_stds[max_class][1]) |
|
|
| if max_value > threshold: |
| y_pred.append(max_class) |
| else: |
| y_pred.append(data.unseen_label_id) |
|
|
| return np.array(y_pred) |
| |
| def fit(self, prob_pos_X): |
| prob_pos = [p for p in prob_pos_X] + [2 - p for p in prob_pos_X] |
| pos_mu, pos_std = dist_model.fit(prob_pos) |
| return pos_mu, pos_std |
|
|
| def cal_mu_std(self, y_prob, trues, num_labels): |
|
|
| mu_stds = [] |
| for i in range(num_labels): |
| pos_mu, pos_std = self.fit(y_prob[trues == i, i]) |
| mu_stds.append([pos_mu, pos_std]) |
|
|
| return mu_stds |
| |
