def validate_official(args, data_loader, model, global_stats, offsets, texts, answers): """Run one full official validation. Uses exact spans and same exact match/F1 score computation as in the SQuAD script. Extra arguments: offsets: The character start/end indices for the tokens in each context. texts: Map of qid --> raw text of examples context (matches offsets). answers: Map of qid --> list of accepted answers. """ clean_id_file = open(os.path.join(DATA_DIR, "clean_qids.txt"), "w+") eval_time = utils.Timer() f1 = utils.AverageMeter() exact_match = utils.AverageMeter() # Run through examples examples = 0 bad_examples = 0 for ex in data_loader: ex_id, batch_size = ex[-1], ex[0].size(0) chosen_offset = ex[-2] pred_s, pred_e, _ = model.predict(ex) for i in range(batch_size): if pred_s[i][0] >= len(offsets[ex_id[i]]) or pred_e[i][0] >= len( offsets[ex_id[i]]): bad_examples += 1 continue if args.use_sentence_selector: s_offset = chosen_offset[i][pred_s[i][0]][0] e_offset = chosen_offset[i][pred_e[i][0]][1] else: s_offset = offsets[ex_id[i]][pred_s[i][0]][0] e_offset = offsets[ex_id[i]][pred_e[i][0]][1] prediction = texts[ex_id[i]][s_offset:e_offset] # Compute metrics ground_truths = answers[ex_id[i]] exact_match.update( utils.metric_max_over_ground_truths(utils.exact_match_score, prediction, ground_truths)) f1.update( utils.metric_max_over_ground_truths(utils.f1_score, prediction, ground_truths)) f1_example = utils.metric_max_over_ground_truths( utils.f1_score, prediction, ground_truths) if f1_example != 0: clean_id_file.write(ex_id + "\n") examples += batch_size clean_id_file.close() logger.info('dev valid official: Epoch = %d | EM = %.2f | ' % (global_stats['epoch'], exact_match.avg * 100) + 'F1 = %.2f | examples = %d | valid time = %.2f (s)' % (f1.avg * 100, examples, eval_time.time())) logger.info('Bad Offset Examples during official eval: %d' % bad_examples) return {'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100}
def eval_accuracies_rc(pred_s, target_s, pred_e, target_e): """An unofficial evalutation helper. Compute exact start/end/complete match accuracies for a batch. """ # Convert 1D tensors to lists of lists (compatibility) if torch.is_tensor(target_s): target_s = [[e] for e in target_s] target_e = [[e] for e in target_e] # Compute accuracies from targets batch_size = len(pred_s) start = utils.AverageMeter() end = utils.AverageMeter() em = utils.AverageMeter() for i in range(batch_size): # Start matches if pred_s[i] in target_s[i]: start.update(1) else: start.update(0) # End matches if pred_e[i] in target_e[i]: end.update(1) else: end.update(0) # Both start and end match if any([1 for _s, _e in zip(target_s[i], target_e[i]) if _s == pred_s[i] and _e == pred_e[i]]): em.update(1) else: em.update(0) return start.avg * 100, end.avg * 100, em.avg * 100
def validate_unofficial(args, data_loader, model, global_stats, mode): """Run one full unofficial validation. Unofficial = doesn't use SQuAD script. """ eval_time = utils.Timer() start_acc = utils.AverageMeter() end_acc = utils.AverageMeter() exact_match = utils.AverageMeter() # Make predictions examples = 0 for ex in data_loader: batch_size = ex[0].size(0) pred_s, pred_e, _ = model.predict(ex) target_s, target_e = ex[-3:-1] # We get metrics for independent start/end and joint start/end accuracies = eval_accuracies(pred_s, target_s, pred_e, target_e) start_acc.update(accuracies[0], batch_size) end_acc.update(accuracies[1], batch_size) exact_match.update(accuracies[2], batch_size) # If getting train accuracies, sample max 10k examples += batch_size if mode == 'train' and examples >= 1e4: break logger.info('%s valid unofficial: Epoch = %d | start = %.2f | ' % (mode, global_stats['epoch'], start_acc.avg) + 'end = %.2f | exact = %.2f | examples = %d | ' % (end_acc.avg, exact_match.avg, examples) + 'valid time = %.2f (s)' % eval_time.time()) return {'exact_match': exact_match.avg}
def validate_unofficial(args, data_loader, model, global_stats, mode): """Run one full unofficial validation. Unofficial = doesn't use SQuAD script. """ from sklearn.metrics import roc_auc_score, f1_score eval_time = utils.Timer() trigger_acc = utils.AverageMeter() eval_time = utils.Timer() start_acc = utils.AverageMeter() end_acc = utils.AverageMeter() exact_match = utils.AverageMeter() # Make predictions all_pred = [] all_pred_label = [] all_gt = [] examples = 0 for ex in data_loader: batch_size = ex[0].size(0) pred_score, pred_label, pred_s, pred_e = model.predict(ex) target_s, target_e = ex[-4:-2] accuracies = eval_accuracies_rc(pred_s, target_s, pred_e, target_e) start_acc.update(accuracies[0], batch_size) end_acc.update(accuracies[1], batch_size) exact_match.update(accuracies[2], batch_size) gt_label = ex[-1] all_pred.extend([x[1] for x in pred_score]) all_gt.extend(gt_label) all_pred_label.extend(pred_label) # We get metrics for independent start/end and joint start/end accuracies = eval_accuracies(pred_label, gt_label) trigger_acc.update(accuracies, batch_size) # If getting train accuracies, sample max 10k examples += batch_size # only test train top 10000 if mode == 'train' and examples >= 1e4: break auc_score = roc_auc_score(all_gt, all_pred) f1_scores = f1_score(all_gt, all_pred_label,average=None) logger.info('%s valid unofficial: Epoch = %d | ' % (mode, global_stats['epoch'], ) + 'neg_f1 = %.2f | pos_f1 = %.2f | trigger_auc = %.2f | trigger_acc = %.2f | examples = %d | ' % (f1_scores[0], f1_scores[1], auc_score, trigger_acc.avg, examples) + 'valid time = %.2f (s)' % eval_time.time()) return {'auc': auc_score, 'trigger_acc': trigger_acc.avg}
def validate_official(args, data_loader, model, global_stats, offsets, texts, answers, mode="dev"): """Run one full official validation. Uses exact spans and same exact match/F1 score computation as in the SQuAD script. Extra arguments: offsets: The character start/end indices for the tokens in each context. texts: Map of qid --> raw text of examples context (matches offsets). answers: Map of qid --> list of accepted answers. """ eval_time = utils.Timer() f1 = utils.AverageMeter() exact_match = utils.AverageMeter() # Run through examples examples = 0 for ex in data_loader: ex_id, batch_size = ex[-1], ex[0].size(0) pred_s, pred_e, _ = model.predict(ex) for i in range(batch_size): s_offset = offsets[ex_id[i]][pred_s[i][0]][0] e_offset = offsets[ex_id[i]][pred_e[i][0]][1] prediction = texts[ex_id[i]][s_offset:e_offset] # Compute metrics ground_truths = answers[ex_id[i]] exact_match.update( utils.metric_max_over_ground_truths(utils.exact_match_score, prediction, ground_truths)) f1.update( utils.metric_max_over_ground_truths(utils.f1_score, prediction, ground_truths)) examples += batch_size logger.info(mode + ' valid official: Epoch = %d | EM = %.2f | ' % (global_stats['epoch'], exact_match.avg * 100) + 'F1 = %.2f | examples = %d | valid time = %.2f (s)' % (f1.avg * 100, examples, eval_time.time())) return {'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100}
def train(args, data_loader, model, global_stats): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader): ''' print('ex:::: ' + str(ex)) sys.exit() ''' train_loss.update(*model.update(ex)) if idx % args.display_iter == 0: logger.info('train: Epoch = %d | iter = %d/%d | ' % (global_stats['epoch'], idx, len(data_loader)) + 'loss = %.2f | elapsed time = %.2f (s)' % (train_loss.avg, global_stats['timer'].time())) train_loss.reset() logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' % (global_stats['epoch'], epoch_time.time())) # Checkpoint if args.checkpoint: model.checkpoint(args.model_file + '.checkpoint', global_stats['epoch'] + 1)
def train(args, data_loader, data_loader_source, data_loader_target, train_loader_source_Q, train_loader_target_Q, model, global_stats): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader_source): # Calculate n_critic epoch = global_stats['epoch'] n_critic = args.n_critic if n_critic > 0 and ((epoch == 0 and idx <= 25) or (idx % 500 == 0)): n_critic = 10 train_loss.update(*model.update(ex, n_critic, epoch)) if idx % args.display_iter == 0: logger.info('train: Epoch = %d | iter = %d/%d | ' % (global_stats['epoch'], idx, len(data_loader)) + 'loss = %.2f | elapsed time = %.2f (s)' % (train_loss.avg, global_stats['timer'].time())) train_loss.reset() logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' % (global_stats['epoch'], epoch_time.time())) # Checkpoint if args.checkpoint: model.checkpoint(args.model_file + '.checkpoint', global_stats['epoch'] + 1)
def train(args, data_loader, model, global_stats): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader): train_loss.update(*model.update(ex)) if idx % args.display_iter == 0: logger.info('train: Epoch = %d | iter = %d/%d | ' % (global_stats['epoch'], idx, len(data_loader)) + 'loss = %.2f | elapsed time = %.2f (s)' % (train_loss.avg, global_stats['timer'].time())) train_loss.reset() if args.indexcheckpoint != -1 and idx != 0 and idx % args.indexcheckpoint == 0: checkpointName = args.model_file + str( idx / args.indexcheckpoint) + ':' + str( global_stats['epoch']) + '.checkpoint' model.checkpoint(checkpointName, global_stats['epoch']) print(['new checkpoint at : %s' % checkpointName]) logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' % (global_stats['epoch'], epoch_time.time())) # Checkpoint if args.checkpoint: model.checkpoint(args.model_file + '.checkpoint', global_stats['epoch'] + 1)
def eval_accuracies(pred_label, gt_label): """An unofficial evalutation helper. Compute exact start/end/complete match accuracies for a batch. """ trigger_acc = utils.AverageMeter() for i in range(len(pred_label)): trigger_acc.update(1 if pred_label[i]==gt_label[i] else 0) return trigger_acc.avg * 100
def train(args, data_loader, model, global_stats, dev_loader): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader): train_loss.update(*model.update(ex)) global_stats['Loss_Train'] = float(train_loss.avg) if idx % args.display_iter == 0: logger.info('train: Epoch = %d | iter = %d/%d | ' % (global_stats['epoch'], idx, len(data_loader)) + 'loss = %.2f | elapsed time = %.2f (s)' % (train_loss.avg, global_stats['timer'].time())) train_loss.reset() ####### Fix this later if args.show_dev_loss: dev_loss = utils.AverageMeter() for idx, ex in enumerate(dev_loader): if args.cuda: inputs = [ e if e is None else Variable(e.cuda(async=True)) for e in ex[:5] ] target_s = Variable(ex[5].cuda(async=True)) target_e = Variable(ex[6].cuda(async=True)) else: print("No cudaaa") inputs = [e if e is None else e for e in ex[:5]] target_s = ex[5] target_e = ex[6] score_s, score_e = model.network(*inputs) loss = F.nll_loss(score_s, target_s) + F.nll_loss( score_e, target_e) dev_loss.update(loss.data[0], ex[0].size(0))
def eval_accuracies(pred_s, target_s): """An unofficial evalutation helper. Compute exact start/end/complete match accuracies for a batch. """ # Convert 1D tensors to lists of lists (compatibility) if torch.is_tensor(target_s): target_s = [[e] for e in target_s] #target_e = [[e] for e in target_e] #print('pred_s: ' + str(pred_s) + ' target_s: ' + str(target_s)) # Compute accuracies from targets batch_size = len(pred_s) start = utils.AverageMeter() #end = utils.AverageMeter() em = utils.AverageMeter() for i in range(batch_size): # Start matches #print('type: ' + str(type(pred_s[i])) + 'pred_s[i]: ' + str(pred_s[i])) #print('type: ' + str(type(target_s[i])) + 'target_s[i]: ' + str(target_s[i])) if pred_s[i] in target_s[i]: start.update(1) else: start.update(0) ''' # End matches if pred_e[i] in target_e[i]: end.update(1) else: end.update(0) ''' # Both start and end match if any([1 for _s in target_s[i] if _s == pred_s[i]]): em.update(1) else: em.update(0) return start.avg * 100, em.avg * 100
def train(args, data_loader, model, global_stats): """Run through one epoch of model training with the provided data loader.""" # Initialize meters + timers train_loss = utils.AverageMeter() epoch_time = utils.Timer() # Run one epoch for idx, ex in enumerate(data_loader): train_loss.update(*model.update(ex)) if idx % args.display_iter == 0: logger.info("train: Epoch = %d | iter = %d/%d | " % (global_stats["epoch"], idx, len(data_loader)) + "loss = %.2f | elapsed time = %.2f (s)" % (train_loss.avg, global_stats["timer"].time())) train_loss.reset() logger.info("train: Epoch %d done. Time for epoch = %.2f (s)" % (global_stats["epoch"], epoch_time.time())) # Checkpoint if args.checkpoint: model.checkpoint(args.model_file + ".checkpoint", global_stats["epoch"] + 1)
def validate_adversarial(args, model, global_stats, mode="dev"): # create dataloader for dev sets, load thier jsons, integrate the function for idx, dataset_file in enumerate(args.adv_dev_json): predictions = {} logger.info("Validating Adversarial Dataset %s" % dataset_file) exs = utils.load_data(args, args.adv_dev_file[idx]) logger.info('Num dev examples = %d' % len(exs)) ## Create dataloader dev_dataset = reader_data.ReaderDataset(exs, model, single_answer=False) if args.sort_by_len: dev_sampler = reader_data.SortedBatchSampler(dev_dataset.lengths(), args.test_batch_size, shuffle=False) else: dev_sampler = torch.utils.data.sampler.SequentialSampler( dev_dataset) if args.use_sentence_selector: dev_batcher = reader_vector.sentence_batchifier( model, single_answer=False) #batching_function = dev_batcher.batchify batching_function = reader_vector.batchify else: batching_function = reader_vector.batchify dev_loader = torch.utils.data.DataLoader( dev_dataset, batch_size=args.test_batch_size, sampler=dev_sampler, num_workers=args.data_workers, collate_fn=batching_function, pin_memory=args.cuda, ) texts = utils.load_text(dataset_file) offsets = {ex['id']: ex['offsets'] for ex in exs} answers = utils.load_answers(dataset_file) eval_time = utils.Timer() f1 = utils.AverageMeter() exact_match = utils.AverageMeter() examples = 0 bad_examples = 0 for ex in dev_loader: ex_id, batch_size = ex[-1], ex[0].size(0) chosen_offset = ex[-2] pred_s, pred_e, _ = model.predict(ex) for i in range(batch_size): if pred_s[i][0] >= len( offsets[ex_id[i]]) or pred_e[i][0] >= len( offsets[ex_id[i]]): bad_examples += 1 continue if args.use_sentence_selector: s_offset = chosen_offset[i][pred_s[i][0]][0] e_offset = chosen_offset[i][pred_e[i][0]][1] else: s_offset = offsets[ex_id[i]][pred_s[i][0]][0] e_offset = offsets[ex_id[i]][pred_e[i][0]][1] prediction = texts[ex_id[i]][s_offset:e_offset] if args.select_k > 1: prediction = "" offset_subset = chosen_offset[i][pred_s[i][0]:pred_e[i][0]] for enum_, o in enumerate(offset_subset): prediction += texts[ex_id[i]][o[0]:o[1]] + " " prediction = prediction.strip() predictions[ex_id[i]] = prediction ground_truths = answers[ex_id[i]] exact_match.update( utils.metric_max_over_ground_truths( utils.exact_match_score, prediction, ground_truths)) f1.update( utils.metric_max_over_ground_truths( utils.f1_score, prediction, ground_truths)) examples += batch_size logger.info( 'dev valid official for dev file %s : Epoch = %d | EM = %.2f | ' % (dataset_file, global_stats['epoch'], exact_match.avg * 100) + 'F1 = %.2f | examples = %d | valid time = %.2f (s)' % (f1.avg * 100, examples, eval_time.time())) orig_f1_score = 0.0 orig_exact_match_score = 0.0 adv_f1_scores = {} # Map from original ID to F1 score adv_exact_match_scores = { } # Map from original ID to exact match score adv_ids = {} all_ids = set() # Set of all original IDs f1 = exact_match = 0 dataset = json.load(open(dataset_file))['data'] for article in dataset: for paragraph in article['paragraphs']: for qa in paragraph['qas']: orig_id = qa['id'].split('-')[0] all_ids.add(orig_id) if qa['id'] not in predictions: message = 'Unanswered question ' + qa[ 'id'] + ' will receive score 0.' # logger.info(message) continue ground_truths = list( map(lambda x: x['text'], qa['answers'])) prediction = predictions[qa['id']] cur_exact_match = utils.metric_max_over_ground_truths( utils.exact_match_score, prediction, ground_truths) cur_f1 = utils.metric_max_over_ground_truths( utils.f1_score, prediction, ground_truths) if orig_id == qa['id']: # This is an original example orig_f1_score += cur_f1 orig_exact_match_score += cur_exact_match if orig_id not in adv_f1_scores: # Haven't seen adversarial example yet, so use original for adversary adv_ids[orig_id] = orig_id adv_f1_scores[orig_id] = cur_f1 adv_exact_match_scores[orig_id] = cur_exact_match else: # This is an adversarial example if (orig_id not in adv_f1_scores or adv_ids[orig_id] == orig_id or adv_f1_scores[orig_id] > cur_f1): # Always override if currently adversary currently using orig_id adv_ids[orig_id] = qa['id'] adv_f1_scores[orig_id] = cur_f1 adv_exact_match_scores[orig_id] = cur_exact_match orig_f1 = 100.0 * orig_f1_score / len(all_ids) orig_exact_match = 100.0 * orig_exact_match_score / len(all_ids) adv_exact_match = 100.0 * sum( adv_exact_match_scores.values()) / len(all_ids) adv_f1 = 100.0 * sum(adv_f1_scores.values()) / len(all_ids) logger.info( "For the file %s Original Exact Match : %.4f ; Original F1 : : %.4f | " % (dataset_file, orig_exact_match, orig_f1) + "Adversarial Exact Match : %.4f ; Adversarial F1 : : %.4f " % (adv_exact_match, adv_f1))
def validate_official(args, data_loader, model, global_stats, offsets, texts, questions, answers): """Run one full official validation. Uses exact spans and same exact match/F1 score computation as in the SQuAD script. Extra arguments: offsets: The character start/end indices for the tokens in each context. texts: Map of qid --> raw text of examples context (matches offsets). answers: Map of qid --> list of accepted answers. """ eval_time = utils.Timer() f1 = utils.AverageMeter() exact_match = utils.AverageMeter() # Run through examples examples = 0 em_false = {} # cid -> (context, [(qid, question, answer)...]) predictions = {} # qid -> prediction for ex in data_loader: ex_id, batch_size = ex[-1], ex[0].size(0) pred_s, pred_e, _ = model.predict(ex) for i in range(batch_size): s_offset = offsets[ex_id[i]][pred_s[i][0]][0] e_offset = offsets[ex_id[i]][pred_e[i][0]][1] prediction = texts[ex_id[i]][1][s_offset:e_offset] cid = texts[ex_id[i]][0] predictions[ex_id[i]] = prediction # Compute metrics ground_truths = answers[ex_id[i]] em_score = utils.metric_max_over_ground_truths( utils.exact_match_score, prediction, ground_truths) if em_score < 1: if cid not in em_false: em_false[cid] = { 'text': texts[ex_id[i]][1], 'qa': [{ 'qid': ex_id[i], 'question': questions[ex_id[i]], 'answers': answers[ex_id[i]], 'prediction': prediction }] } else: em_false[cid]['qa'].append({ 'qid': ex_id[i], 'question': questions[ex_id[i]], 'answers': answers[ex_id[i]], 'prediction': prediction }) exact_match.update(em_score) f1.update( utils.metric_max_over_ground_truths(utils.f1_score, prediction, ground_truths)) examples += batch_size logger.info('dev valid official: Epoch = %d | EM = %.2f | ' % (global_stats['epoch'], exact_match.avg * 100) + 'F1 = %.2f | examples = %d | valid time = %.2f (s)' % (f1.avg * 100, examples, eval_time.time())) return { 'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100 }, em_false, predictions