def log_to_csv_with_auc_accuracy(y_true, y_pred, y_score, csv_log_file_path, identity_info="dataset"): labels = [0, 1] result = precision_recall_fscore_support(y_true, y_pred) row = [] row.append(identity_info) # neg row.append('label ' + str(labels[0]) + ":") row.append(result[0][0]) row.append(result[1][0]) row.append(result[2][0]) row.append(result[3][0]) row.append(' ') # pos row.append('label ' + str(labels[1]) + ":") row.append(result[0][1]) row.append(result[1][1]) row.append(result[2][1]) row.append(result[3][1]) row.append(' ') # auc and accuracy y_pos_score = transform.map_func(y_score, lambda p : p[1]) auc = metrics.roc_auc_score(y_true, y_pos_score) row.append(auc) accuracy = metrics.accuracy_score(y_true, y_pred) row.append(accuracy) csv_handler.append_row(csv_log_file_path, row)
def log_to_csv_multi_f1(y_true, y_pred, csv_log_file_path, identity_info="dataset"): result = precision_recall_fscore_support(y_true, y_pred) row = [] row.append(identity_info) multi_f1 = list(result[2]) row += multi_f1 csv_handler.append_row(csv_log_file_path, row)
def log_to_csv(y_true, y_pred, csv_log_file_path, identity_info="dataset"): labels = [0, 1] result = precision_recall_fscore_support(y_true, y_pred) row = [] row.append(identity_info) # neg row.append('label ' + str(labels[0]) + ":") row.append(result[0][0]) row.append(result[1][0]) row.append(result[2][0]) row.append(result[3][0]) row.append(' ') # pos row.append('label ' + str(labels[1]) + ":") row.append(result[0][1]) row.append(result[1][1]) row.append(result[2][1]) row.append(result[3][1]) csv_handler.append_row(csv_log_file_path, row)
#('clf', LogisticRegression(class_weight='balanced', random_state=seed, solver='liblinear')), ('clf', LogisticRegression(random_state=seed, solver='liblinear')), ]) text_clf.fit(X_train, y_train) train_finish_time = time.time() train_duration = train_finish_time - start_time print("train time is " + str(train_finish_time - start_time)) print("predicting...") predicted = text_clf.predict(X_dev) predicted_proba = text_clf.predict_proba(X_dev) assert (len(predicted_proba) == len(X_dev)) assert (len(X_dev) == len(y_dev)) print("logging...") csv_handler.append_row(output_path, ['score_0', 'score_1', 'predict', 'text', 'ground']) result = [] for i in range(len(predicted_proba)): score_0 = predicted_proba[i][0] score_1 = predicted_proba[i][1] predict = predicted[i] text = X_dev[i] ground = y_dev[i] result.append([score_0, score_1, predict, text, ground]) csv_handler.csv_writelines(output_path, result)
def main(): start_time = time.time() parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.") parser.add_argument("--bert_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, " "bert-base-multilingual-cased, bert-base-chinese.") parser.add_argument("--task_name", default=None, type=str, required=True, help="The name of the task to train.") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.") parser.add_argument("--log_file_path", default=None, type=str, required=True, help="Define log file path.") ## Other parameters parser.add_argument("--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from s3") parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument("--warmup_proportion", default=0.1, type=float, help="Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--gradient_accumulation_steps', type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument('--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', type=float, default=0, help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") args = parser.parse_args() if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() processors = { "sst-2": Sst2Processor, } output_modes = { "cola": "classification", "sst-2": "classification", #"sts-b": "regression", } if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN) logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format( device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( args.gradient_accumulation_steps)) args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if not args.do_train and not args.do_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train: raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) task_name = args.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() output_mode = output_modes[task_name] label_list = processor.get_labels() #label_weight = WeightClassCSV(args.data_dir + "/train.csv").get_weights(label_list) num_labels = len(label_list) tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) train_examples = None num_train_optimization_steps = None if args.do_train: train_examples = processor.get_train_examples(args.data_dir) num_train_optimization_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs if args.local_rank != -1: num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() # Prepare model cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank)) model = BertForSequenceClassification.from_pretrained(args.bert_model, cache_dir=cache_dir, num_labels=num_labels) if args.fp16: model.half() model.to(device) if args.local_rank != -1: try: from apex.parallel import DistributedDataParallel as DDP except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) # Prepare optimizer if args.do_train: param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] if args.fp16: try: from apex.optimizers import FP16_Optimizer from apex.optimizers import FusedAdam except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, max_grad_norm=1.0) if args.loss_scale == 0: optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) else: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion, t_total=num_train_optimization_steps) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=num_train_optimization_steps) global_step = 0 nb_tr_steps = 0 tr_loss = 0 if args.do_train: train_features = convert_examples_to_features( train_examples, label_list, args.max_seq_length, tokenizer, output_mode) logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Batch size = %d", args.train_batch_size) logger.info(" Num steps = %d", num_train_optimization_steps) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) if output_mode == "classification": all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long) elif output_mode == "regression": all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: train_sampler = RandomSampler(train_data) else: train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size, drop_last=True) model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch # define a new function to compute loss values for both output_modes logits = model(input_ids, segment_ids, input_mask, labels=None) if output_mode == "classification": loss_fct = CrossEntropyLoss() #loss_fct = CrossEntropyLoss(weight = torch.tensor(label_weight).to(device)) loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1)) elif output_mode == "regression": loss_fct = MSELoss() loss = loss_fct(logits.view(-1), label_ids.view(-1)) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: optimizer.backward(loss) else: loss.backward() tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() optimizer.zero_grad() global_step += 1 if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Save a trained model, configuration and tokenizer model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self # If we save using the predefined names, we can load using `from_pretrained` output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) torch.save(model_to_save.state_dict(), output_model_file) model_to_save.config.to_json_file(output_config_file) tokenizer.save_vocabulary(args.output_dir) # Load a trained model and vocabulary that you have fine-tuned model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels) tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) else: model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels) model.to(device) train_finish_time = time.time() train_overall_time = train_finish_time - start_time if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features( eval_examples, label_list, args.max_seq_length, tokenizer, output_mode) logger.info("***** Running evaluation *****") logger.info(" Num examples = %d", len(eval_examples)) logger.info(" Batch size = %d", args.eval_batch_size) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) if output_mode == "classification": all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) elif output_mode == "regression": all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss = 0 nb_eval_steps = 0 preds = [] for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): logits = model(input_ids, segment_ids, input_mask, labels=None) # create eval loss and other metric required by the task if output_mode == "classification": loss_fct = CrossEntropyLoss() #loss_fct = CrossEntropyLoss(weight = torch.tensor(label_weight).to(device)) tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1)) elif output_mode == "regression": loss_fct = MSELoss() tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1)) eval_loss += tmp_eval_loss.mean().item() nb_eval_steps += 1 if len(preds) == 0: preds.append(logits.detach().cpu().numpy()) else: preds[0] = np.append( preds[0], logits.detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps preds = preds[0] original_preds = preds.copy() if output_mode == "classification": preds = np.argmax(preds, axis=1) elif output_mode == "regression": preds = np.squeeze(preds) result = compute_metrics(task_name, preds, all_label_ids.numpy()) auc = compute_auc(original_preds, all_label_ids.numpy()) preds_output_path = os.path.join(args.output_dir, "pred.csv") write_preds(original_preds, all_label_ids.numpy(), os.path.join(args.data_dir, "dev.csv"), preds_output_path) result['auc'] = auc loss = tr_loss/global_step if args.do_train else None result['eval_loss'] = eval_loss result['global_step'] = global_step result['loss'] = loss result['train_second'] = train_overall_time result['test_second'] = time.time() - train_finish_time result['runtime_second'] = time.time() - start_time output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) # write to log file #log_file_path = "./result/bert.log" log_file_path = args.log_file_path log_row = [] log_row.append(args.data_dir) log_row.append(result['precision']) log_row.append(result['recall']) log_row.append(result['f1']) log_row.append(result['train_second']) log_row.append(result['test_second']) ## append metric report for negative label #(pre, rec, fs, sup) = precision_recall_fscore_support(y_true=all_label_ids.numpy(), y_pred=preds, pos_label=0, average='binary') #log_row.append("") #log_row.append(pre) #log_row.append(rec) #log_row.append(fs) csv_handler.append_row(log_file_path, log_row)
train_duration = train_finish_time - start_time print("train time is " + str(train_finish_time - start_time)) print("predicting...") predicted = text_clf.predict(X_dev) predicted_proba = text_clf.predict_proba(X_dev) assert (len(predicted_proba) == len(X_dev)) assert (len(X_dev) == len(y_dev)) print("logging...") (precision, recall, fscore, support) = metrics.precision_recall_fscore_support(y_dev, predicted) row = [] row.append(sys.argv[1]) row.append(precision[1]) row.append(recall[1]) row.append(fscore[1]) pos_predicted = transform.map_func(predicted_proba, lambda p: p[1]) auc = metrics.roc_auc_score(y_dev, pos_predicted) row.append(auc) accuracy = metrics.accuracy_score(y_dev, predicted) row.append(accuracy) csv_handler.append_row(log_file_path, row)
csv_dataset = csv_handler.csv_readlines(csv_input_path) y_true = transform.map_func(csv_dataset, lambda row: int(row[y_true_col])) y_pred_score = transform.map_func(csv_dataset, lambda row: float(row[y_pred_col])) #y_pred_score = transform.map_func(y_pred_score, lambda score : 1 / (1 + math.exp(-score))) thred_col = [] if thred_method == "min_max_even": thred_col = get_threds_by_min_max_even(y_pred_score, num_threds) else: # sorted score even slot thred_col = get_threds_by_sorted_score_equal_length( y_pred_score, num_threds) if print_header == '1': csv_handler.append_row( csv_output_path, ['threshold', 'precision', 'recall', 'fscore', 'support']) for thred in thred_col: y_pred = transform.map_func(y_pred_score, lambda score: 1 if score >= thred else 0) result = precision_recall_fscore_support(y_true, y_pred) csv_handler.append_row( csv_output_path, [thred, result[0][1], result[1][1], result[2][1], result[3][1]])
print("Evaluating ...") model.eval() predicts = [] golds = [] with torch.no_grad(): for idx, batch in enumerate(tqdm(test_iter, desc="Iteration")): inputs, labels = batch.sent, batch.label inputs = inputs.to(device) logits = model(inputs) predict = torch.argmax(logits, dim=1).data.cpu().numpy() predicts += list(predict) golds += list(labels.data.cpu().numpy()) precision, recall, f1 = F1(predicts, golds) print("Precision: %f, Recall: %f, F1: %f" % (precision, recall, f1)) train_time = train_overall_time test_time = time.time() - train_finish_time (precision, recall, fscore, support) = metrics.precision_recall_fscore_support(golds, predicts) log_row = [] log_row.append(args.dataset) log_row.append(precision[1]) log_row.append(recall[1]) log_row.append(fscore[1]) log_row.append(train_time) log_row.append(test_time) csv_handler.append_row(args.log_file, log_row)