1_bertClassifcation.py

# coding=utf-8


from __future__ import absolute_import, division, print_function

import argparse
import glob
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tensorboardX import SummaryWriter
from tqdm import tqdm, trange
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset


from transformers import AdamW, WarmupLinearSchedule
from transformers import (WEIGHTS_NAME, BertConfig,
                                  BertForSequenceClassification, BertTokenizer)
from transformers.data.processors.utils import DataProcessor, InputExample,InputFeatures

import pandas as pd
import torch.nn.functional as F
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score, precision_score, recall_score

logger = logging.getLogger(__name__)
 
 
def convert_examples_to_features(examples, tokenizer,
                                      max_length=512,
                                      task=None,
                                      label_list=None,
                                      output_mode=None,
                                      pad_on_left=False,
                                      pad_token=0,
                                      pad_token_segment_id=0,
                                      mask_padding_with_zero=True):
 

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d" % (ex_index))
 

        inputs = tokenizer.encode_plus(
            example.text_a,
            example.text_b,
            add_special_tokens=True,
            max_length=max_length,
            truncate_first_sequence=False  # We're truncating the first sequence in priority
        )
        # import code
        # code.interact(local=locals())
        input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding_length = max_length - len(input_ids)
        if pad_on_left:
            input_ids = ([pad_token] * padding_length) + input_ids
            attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
        else:
            input_ids = input_ids + ([pad_token] * padding_length)
            attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
            token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)
        # import code
        # code.interact(local=locals())
        # print()
        assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
        assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(len(attention_mask), max_length)
        assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(len(token_type_ids), max_length)

        label = label_map[example.label]

        if ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s" % (example.guid))
            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
            logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
            logger.info("label: %s (id = %d)" % (example.label, label))

        features.append(
                InputFeatures(input_ids=input_ids,
                              attention_mask=attention_mask,
                              token_type_ids=token_type_ids,
                              label=label))

    return features


class FaqProcessor(DataProcessor):
    
    def get_train_examples(self, data_dir):
        return self._create_examples(os.path.join(data_dir, 'part2.csv'))
    
    def get_dev_examples(self, data_dir):
        return self._create_examples(os.path.join(data_dir, 'part3.csv'))

    def get_labels(self):
        return [1,2,3,4,5]

    def _create_examples(self, path):
        
        df = pd.read_csv(path, encoding='utf-8')
        examples = []
        summary = df['summary'].tolist()
        text = df['short_text'].tolist()
        humanlable = df['label'].astype('int').tolist()

        for i in range(len(humanlable)):
            examples.append(
                InputExample(guid=i, text_a=text[i], text_b=summary[i], label=humanlable[i]))
        return examples
    

def simple_accuracy(preds, labels):
    return (preds == labels).mean()


def acc_and_f1(preds, labels):
    acc = simple_accuracy(preds, labels)
    f1 = f1_score( y_true=labels, y_pred=preds, average='macro' )
    p = precision_score(y_true=labels, y_pred=preds, average='macro')
    r = recall_score(y_true=labels, y_pred=preds, average='macro')
    return {
        "acc": acc,
        "f1": f1,
        "precision": p,
        "recall" : r,
    }


# def pearson_and_spearman(preds, labels):
#     pearson_corr = pearsonr(preds, labels)[0]
#     spearman_corr = spearmanr(preds, labels)[0]
#     return {
#         "pearson": pearson_corr,
#         "spearmanr": spearman_corr,
#         "corr": (pearson_corr + spearman_corr) / 2,
#     }

# def acc_f1_pea_spea(preds, labels):
#     acc_f1 = acc_and_f1(preds, labels)
#     pea_spea = pearson_and_spearman(preds,labels)
#     return {**acc_f1, **pea_spea}


def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)



def train(args, processor, model, tokenizer):
    """ Train the model """
    tb_writer = SummaryWriter()

    train_dataset = load_and_cache_examples(args, processor, tokenizer, evaluate=False)
    train_sampler = RandomSampler(train_dataset) 
    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)

    if args.max_steps > 0:
        t_total = args.max_steps
        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
    else:
        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs

    # Prepare optimizer and schedule (linear warmup and decay)
    no_decay = ['bias', 'LayerNorm.weight']
    optimizer_grouped_parameters = [
        {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
        ]
    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
    scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
 
   
    # Train!
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", len(train_dataset))
    logger.info("  Num Epochs = %d", args.num_train_epochs)
    logger.info("  Instantaneous batch size per GPU = %d", args.train_batch_size)
    logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
                   args.train_batch_size * args.gradient_accumulation_steps)
    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
    logger.info("  Total optimization steps = %d", t_total)

    global_step = 0
    tr_loss, logging_loss = 0.0, 0.0
    model.zero_grad()
    train_iterator = trange(int(args.num_train_epochs), desc="Epoch")
    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
    for _ in train_iterator:
        epoch_iterator = tqdm(train_dataloader, desc="Iteration")
        for step, batch in enumerate(epoch_iterator):
            model.train()
            batch = tuple(t.to(args.device) for t in batch)
            inputs = {'input_ids':      batch[0],
                      'attention_mask': batch[1],
                      'token_type_ids': batch[2],
                      'labels':         batch[3]}
           
            outputs = model(**inputs)
            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)

          
            if args.gradient_accumulation_steps > 1:
                loss = loss / args.gradient_accumulation_steps

            
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

            tr_loss += loss.item()
            if (step + 1) % args.gradient_accumulation_steps == 0:
                optimizer.step()
                scheduler.step()  # Update learning rate schedule
                model.zero_grad()
                global_step += 1

                if args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    # Log metrics
                    if args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
                        results = evaluate(args, processor, model, tokenizer)
                        for key, value in results.items():
                            tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
                    tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
                    tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
                    logging_loss = tr_loss

                if args.save_steps > 0 and global_step % args.save_steps == 0:
                    # Save model checkpoint
                    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
                    model_to_save.save_pretrained(output_dir)
                    torch.save(args, os.path.join(output_dir, 'training_args.bin'))
                    logger.info("Saving model checkpoint to %s", output_dir)

            if args.max_steps > 0 and global_step > args.max_steps:
                epoch_iterator.close()
                break
        if args.max_steps > 0 and global_step > args.max_steps:
            train_iterator.close()
            break

    tb_writer.close()

    return global_step, tr_loss / global_step


def evaluate(args, processor, model, tokenizer, prefix=""):
    # Loop to handle MNLI double evaluation (matched, mis-matched)
    # eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,)
    eval_output_dir = args.output_dir
    results = {}
    # for  eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
    eval_dataset = load_and_cache_examples(args, processor, tokenizer, evaluate=True)

    if not os.path.exists(eval_output_dir) :
        os.makedirs(eval_output_dir)

    # Note that DistributedSampler samples randomly
    eval_sampler = SequentialSampler(eval_dataset)  
    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

    # Eval!
    logger.info("***** Running evaluation {}*****".format(prefix))
    logger.info("  Num examples = %d", len(eval_dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
    eval_loss = 0.0
    nb_eval_steps = 0
    preds = None
    out_label_ids = None
    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        model.eval()
        batch = tuple(t.to(args.device) for t in batch)

        with torch.no_grad():
            inputs = {'input_ids':      batch[0],
                        'attention_mask': batch[1],
                        'token_type_ids': batch[2],
                        'labels':         batch[3]}
            outputs = model(**inputs)
            tmp_eval_loss, logits = outputs[:2]

            eval_loss += tmp_eval_loss.mean().item()
        nb_eval_steps += 1
        if preds is None:
            preds = logits.detach().cpu().numpy()
            out_label_ids = inputs['labels'].detach().cpu().numpy()
        else:
            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
            out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0)

    eval_loss = eval_loss / nb_eval_steps
    preds = np.argmax(preds, axis=1)
    result = acc_and_f1(preds, out_label_ids)
    results.update(result)

    output_eval_file = os.path.join(eval_output_dir, "eval_results.txt")
    with open(output_eval_file, "w") as writer:
        logger.info("***** Eval results {} *****".format(prefix))
        for key in sorted(result.keys()):
            logger.info("  %s = %s", key, str(result[key]))
            writer.write("%s = %s\n" % (key, str(result[key])))

    return results


def load_and_cache_examples(args, processor, tokenizer, evaluate=False):

    # Load data features from cache or dataset file
    cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}'.format(
        'dev' if evaluate else 'train',
        str(args.max_seq_length)))  
    if os.path.exists(cached_features_file):
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
        logger.info("Creating features from dataset file at %s", args.data_dir)
        label_list = processor.get_labels()
        examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
        features = convert_examples_to_features(
                                examples=examples,
                                tokenizer=tokenizer,
                                max_length=args.max_seq_length,
                                label_list=label_list,
                                output_mode='classification',
                                pad_on_left=False,
                                pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
                                pad_token_segment_id=0)
        
        logger.info("Saving features into cached file %s", cached_features_file)
        torch.save(features, cached_features_file)


    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
    all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)

    all_labels = torch.tensor([f.label for f in features], dtype=torch.long)

    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
    return dataset


def main():
    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("--model_name_or_path", default=None, type=str, required=True,
    #                     help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
    parser.add_argument('--model_path', default='./cache_down', type=str, required=True)

    parser.add_argument("--output_dir", default=None, type=str, required=True,
                        help="The output directory where the model predictions and checkpoints will be written.")

    ## Other parameters
    parser.add_argument("--config_name", default="", type=str,
                        help="Pretrained config name or path if not the same as model_name")
    parser.add_argument("--tokenizer_name", default="", type=str,
                        help="Pretrained tokenizer name or path if not the same as model_name")
    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 tokenization. Sequences longer "
                             "than this will be truncated, sequences shorter 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("--evaluate_during_training", action='store_true',
                        help="Rul evaluation during training at each logging step.")
    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=8, type=int,
                        help="Batch size per GPU/CPU for training.")
    parser.add_argument("--eval_batch_size", default=8, type=int,
                        help="Batch size per GPU/CPU for evaluation.")
    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("--learning_rate", default=5e-5, type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument("--weight_decay", default=0.0, type=float,
                        help="Weight deay if we apply some.")
    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
                        help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float,
                        help="Max gradient norm.")
    parser.add_argument("--num_train_epochs", default=3.0, type=float,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--max_steps", default=-1, type=int,
                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
    parser.add_argument("--warmup_steps", default=0, type=int,
                        help="Linear warmup over warmup_steps.")

    parser.add_argument('--logging_steps', type=int, default=500,
                        help="Log every X updates steps.")
    parser.add_argument('--save_steps', type=int, default=500,
                        help="Save checkpoint every X updates steps.")
    parser.add_argument("--eval_all_checkpoints", action='store_true',
                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
    parser.add_argument('--overwrite_output_dir', action='store_true',
                        help="Overwrite the content of the output directory")
    parser.add_argument('--overwrite_cache', action='store_true',
                        help="Overwrite the cached training and evaluation sets")
    parser.add_argument('--seed', type=int, default=42,
                        help="random seed for initialization")
    parser.add_argument('--do_predict', action='store_true')
    parser.add_argument('--checkpoint', type=str, default='checkpoint-1000')
    
    
    args = parser.parse_args()

    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
        raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
    
    args.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # Setup logging
    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                        datefmt = '%m/%d/%Y %H:%M:%S',
                        level = logging.INFO)

    # Set seed
    set_seed(args)

    # Prepare GLUE task
    processor = FaqProcessor()

    # Training
    if args.do_train:
        label_list = processor.get_labels()
        num_labels = len(label_list)

        # config = BertConfig.from_pretrained('bert-base-chinese', cache_dir='./cache_down', num_labels=num_labels)
        # tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', cache_dir='./cache_down')
        # # model =  BertForSequenceClassification.from_pretrained("./cache_down/pytorch_model.bin", config=config)
        config = BertConfig.from_pretrained(args.model_path, num_labels=num_labels)
        tokenizer = BertTokenizer.from_pretrained(args.model_path)
        model = BertForSequenceClassification.from_pretrained(args.model_path, config=config)
        model.to(args.device)

        logger.info("Training/evaluation parameters %s", args)
        global_step, tr_loss = train(args, processor, model, tokenizer)
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)


        # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
        # Create output directory if needed
        if not os.path.exists(args.output_dir) :
            os.makedirs(args.output_dir)

        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
        model_to_save.save_pretrained(args.output_dir)
        tokenizer.save_pretrained(args.output_dir)

        # Good practice: save your training arguments together with the trained model
        torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))

        # Load a trained model and vocabulary that you have fine-tuned
        model = BertForSequenceClassification.from_pretrained(args.output_dir)
        tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
        model.to(args.device)


    # Evaluation
    results = {}
    if args.do_eval :
        # tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
        tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', cache_dir='./cache_down')
        checkpoints = [args.output_dir]
        if args.eval_all_checkpoints:
            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/pytorch_model.bin', recursive=True)))
            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
        logger.info("Evaluate the following checkpoints: %s", checkpoints)
        print(checkpoints)
        for checkpoint in checkpoints:
            print(checkpoint)
            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
            model = BertForSequenceClassification.from_pretrained(checkpoint)#, config=checkpoint+'/config.json')
            model.to(args.device)
            result = evaluate(args,processor, model, tokenizer, prefix=global_step)
            result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
            results.update(result)

    if args.do_predict:

        tokenizer = BertTokenizer.from_pretrained('bert-base-chinese', cache_dir='./cache_down')
        model = BertForSequenceClassification.from_pretrained(os.path.join(args.output_dir, args.checkpoint))
        model.to(args.device)

        tilte = ['请问有适合新手的理财方案吗?']
        # replies = processor.prepare_replies(os.path.join(args.data_dir, 'train.csv'))
        replies = ['这个要根据你理财的初始金额和能接受的浮动范围来定制理财方案, 比如,你有10万用于理财, 不可以接受本金亏损,那么推荐你使用银行的定期理财',
                   '如果是招行卡，退款一般是3-5个工作日。', 
                   '欢迎通过招行渠道理财，目前招行个人投资理财方式较多：定期、国债、受托理财、基金、黄金等做组合', '房贷利率低',
                   '欢迎通过招行渠道理财,父母多大年龄', 
                   '密码输入次数超过4次 账户被锁住', 
                   '《征信业管理条例》规定：征信机构对个人不良信息的保存期限，自不良行为或者事件终止之日起为5年',
                   '推荐理财网站,可以学习各个方面的理财知识',
                   '新手可以尝试银行的定期理财']
        
        examples = processor._create_one_example(tilte, replies)
        features = convert_one_example_to_features(examples, tokenizer)
        
        # Convert to Tensors and build dataset
        all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
        all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
        all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
       
        with torch.no_grad():
            inputs = {'input_ids':      all_input_ids.to(args.device),
                        'attention_mask': all_attention_mask.to(args.device),
                        'token_type_ids': all_token_type_ids.to(args.device)}
            outputs = model(**inputs)
            logits = outputs[0]
            score = F.softmax(logits,dim=1)
            print(score)
            values,index = score.sort(0,descending=True)

        recommend_answers = []
        for i in range(values.shape[0]):
            if values[i][1] > 0.9 and len(recommend_answers) < 5:
                recommend_answers.append(replies[index[i][1]])
                
        print('\nQuestion: ', tilte[0] + '\n')
        print('\nAll answers for predicts: \n')
        for rep in replies:
            print('\t'+ rep + '\n')
        # print('Recommend answer:', replies[index[0][1]], '\n')
        if len(recommend_answers) == 0:
            print('No recommend answers!')
        else:
            print('Recommend_answers: \n')
            for i in recommend_answers:
                print('\t' + i + '\n')
        

if __name__ == "__main__":
    main()
    # train_test_read('data_fin_new/train_fin.csv','data_fin_new/test_fin.csv')