bert_lstm_ner.py

#! usr/bin/env python3
# -*- coding:utf-8 -*-
"""
Copyright 2018 The Google AI Language Team Authors.
BASED ON Google_BERT.
reference from :zhoukaiyin/

@Author:Macan
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import os
import json
import logging


import tensorflow as tf
import codecs
from tensorflow.contrib.layers.python.layers import initializers
from tensorflow.contrib import estimator
from bert import modeling
from bert import optimization
from bert import tokenization
from lstm_crf_layer import BLSTM_CRF

import tf_metrics
import pickle

os.environ['CUDA_VISIBLE_DEVICES'] = '3'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

flags = tf.flags

FLAGS = flags.FLAGS

if os.name == 'nt':
    bert_path = 'H:\迅雷下载\chinese_L-12_H-768_A-12\chinese_L-12_H-768_A-12'
    root_path = r'C:\workspace\python\BERT-BiLSMT-CRF-NER'
else:
    bert_path = '/home/macan/ml/data/chinese_L-12_H-768_A-12/'
    root_path = '/home/macan/ml/workspace/BERT-BiLSTM-CRF-NER'

flags.DEFINE_string(
    "data_dir", os.path.join(root_path, 'NERdata'),
    "The input datadir.",
)

flags.DEFINE_string(
    "bert_config_file", os.path.join(bert_path, 'bert_config.json'),
    "The config json file corresponding to the pre-trained BERT model."
)

flags.DEFINE_string(
    "task_name", 'ner', "The name of the task to train."
)

flags.DEFINE_string(
    "output_dir", os.path.join(root_path, 'output'),
    "The output directory where the model checkpoints will be written."
)

## Other parameters
flags.DEFINE_string(
    "init_checkpoint", os.path.join(bert_path, 'bert_model.ckpt'),
    "Initial checkpoint (usually from a pre-trained BERT model)."
)

flags.DEFINE_bool(
    "do_lower_case", True,
    "Whether to lower case the input text."
)

flags.DEFINE_integer(
    "max_seq_length", 128,
    "The maximum total input sequence length after WordPiece tokenization."
)

flags.DEFINE_boolean('clean', True, 'remove the files which created by last training')

flags.DEFINE_bool("do_train", True, "Whether to run training."
)
flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

flags.DEFINE_bool("do_eval", True, "Whether to run eval on the dev set.")

flags.DEFINE_bool("do_predict", True, "Whether to run the model in inference mode on the test set.")

flags.DEFINE_integer("train_batch_size", 64, "Total batch size for training.")

flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")

flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

flags.DEFINE_float("num_train_epochs", 15.0, "Total number of training epochs to perform.")
flags.DEFINE_float('droupout_rate', 0.5, 'Dropout rate')
flags.DEFINE_float('clip', 5, 'Gradient clip')
flags.DEFINE_float(
    "warmup_proportion", 0.1,
    "Proportion of training to perform linear learning rate warmup for. "
    "E.g., 0.1 = 10% of training.")

flags.DEFINE_integer("save_checkpoints_steps", 1000,
                     "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 1000,
                     "How many steps to make in each estimator call.")

flags.DEFINE_string("vocab_file", os.path.join(bert_path, 'vocab.txt'),
                    "The vocabulary file that the BERT model was trained on.")

tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")
flags.DEFINE_string('data_config_path', os.path.join(root_path, 'data.conf'),
                    'data config file, which save train and dev config')
# lstm parame
flags.DEFINE_integer('lstm_size', 128, 'size of lstm units')
flags.DEFINE_integer('num_layers', 1, 'number of rnn layers, default is 1')
flags.DEFINE_string('cell', 'lstm', 'which rnn cell used')




class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text, label=None):
        """Constructs a InputExample.

        Args:
          guid: Unique id for the example.
          text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
          label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text = text
        self.label = label

class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_ids, ):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_ids = label_ids
        # self.label_mask = label_mask


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_data(cls, input_file):
        """Reads a BIO data."""
        with codecs.open(input_file, 'r', encoding='utf-8') as f:
            lines = []
            words = []
            labels = []
            for line in f:
                contends = line.strip()
                tokens = contends.split(' ')
                if len(tokens) == 2:
                    word = line.strip().split(' ')[0]
                    label = line.strip().split(' ')[-1]
                else:
                    if len(contends) == 0:
                        l = ' '.join([label for label in labels if len(label) > 0])
                        w = ' '.join([word for word in words if len(word) > 0])
                        lines.append([l, w])
                        words = []
                        labels = []
                        continue
                if contends.startswith("-DOCSTART-"):
                    words.append('')
                    continue
                words.append(word)
                labels.append(label)
            return lines


class NerProcessor(DataProcessor):
    def get_train_examples(self, data_dir):
        return self._create_example(
            self._read_data(os.path.join(data_dir, "train.txt")), "train"
        )

    def get_dev_examples(self, data_dir):
        return self._create_example(
            self._read_data(os.path.join(data_dir, "dev.txt")), "dev"
        )

    def get_test_examples(self, data_dir):
        return self._create_example(
            self._read_data(os.path.join(data_dir, "test.txt")), "test")

    def get_labels(self):
        return ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "X", "[CLS]", "[SEP]"]

    def _create_example(self, lines, set_type):
        examples = []
        for (i, line) in enumerate(lines):
            guid = "%s-%s" % (set_type, i)
            text = tokenization.convert_to_unicode(line[1])
            label = tokenization.convert_to_unicode(line[0])
            if i == 0:
                print(label)
            examples.append(InputExample(guid=guid, text=text, label=label))
        return examples


def write_tokens(tokens, mode):
    """
    将序列解析结果写入到文件中
    只在mode=test的时候启用
    :param tokens:
    :param mode:
    :return:
    """
    if mode == "test":
        path = os.path.join(FLAGS.output_dir, "token_" + mode + ".txt")
        wf = codecs.open(path, 'a', encoding='utf-8')
        for token in tokens:
            if token != "**NULL**":
                wf.write(token + '\n')
        wf.close()


def convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer, mode):
    """
    将一个样本进行分析，然后将字转化为id, 标签转化为id,然后结构化到InputFeatures对象中
    :param ex_index: index
    :param example: 一个样本
    :param label_list: 标签列表
    :param max_seq_length:
    :param tokenizer:
    :param mode:
    :return:
    """
    label_map = {}
    # 1表示从1开始对label进行index化
    for (i, label) in enumerate(label_list, 1):
        label_map[label] = i
    # 保存label->index 的map
    with codecs.open(os.path.join(FLAGS.output_dir, 'label2id.pkl'), 'wb') as w:
        pickle.dump(label_map, w)
    textlist = example.text.split(' ')
    labellist = example.label.split(' ')
    tokens = []
    labels = []
    for i, word in enumerate(textlist):
        # 分词，如果是中文，就是分字
        token = tokenizer.tokenize(word)
        tokens.extend(token)
        label_1 = labellist[i]
        for m in range(len(token)):
            if m == 0:
                labels.append(label_1)
            else:  # 一般不会出现else
                labels.append("X")
    # tokens = tokenizer.tokenize(example.text)
    # 序列截断
    if len(tokens) >= max_seq_length - 1:
        tokens = tokens[0:(max_seq_length - 2)]  # -2 的原因是因为序列需要加一个句首和句尾标志
        labels = labels[0:(max_seq_length - 2)]
    ntokens = []
    segment_ids = []
    label_ids = []
    ntokens.append("[CLS]")  # 句子开始设置CLS 标志
    segment_ids.append(0)
    # append("O") or append("[CLS]") not sure!
    label_ids.append(label_map["[CLS]"])  # O OR CLS 没有任何影响，不过我觉得O 会减少标签个数,不过拒收和句尾使用不同的标志来标注，使用LCS 也没毛病
    for i, token in enumerate(tokens):
        ntokens.append(token)
        segment_ids.append(0)
        label_ids.append(label_map[labels[i]])
    ntokens.append("[SEP]")  # 句尾添加[SEP] 标志
    segment_ids.append(0)
    # append("O") or append("[SEP]") not sure!
    label_ids.append(label_map["[SEP]"])
    input_ids = tokenizer.convert_tokens_to_ids(ntokens)  # 将序列中的字(ntokens)转化为ID形式
    input_mask = [1] * len(input_ids)
    # label_mask = [1] * len(input_ids)
    # padding, 使用
    while len(input_ids) < max_seq_length:
        input_ids.append(0)
        input_mask.append(0)
        segment_ids.append(0)
        # we don't concerned about it!
        label_ids.append(0)
        ntokens.append("**NULL**")
        # label_mask.append(0)
    # print(len(input_ids))
    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length
    assert len(label_ids) == max_seq_length
    # assert len(label_mask) == max_seq_length

    # 打印部分样本数据信息
    if ex_index < 5:
        tf.logging.info("*** Example ***")
        tf.logging.info("guid: %s" % (example.guid))
        tf.logging.info("tokens: %s" % " ".join(
            [tokenization.printable_text(x) for x in tokens]))
        tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
        tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
        tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
        tf.logging.info("label_ids: %s" % " ".join([str(x) for x in label_ids]))
        # tf.logging.info("label_mask: %s" % " ".join([str(x) for x in label_mask]))

    # 结构化为一个类
    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_ids=label_ids,
        # label_mask = label_mask
    )
    # mode='test'的时候才有效
    write_tokens(ntokens, mode)
    return feature


def filed_based_convert_examples_to_features(
        examples, label_list, max_seq_length, tokenizer, output_file, mode=None
):
    """
    将数据转化为TF_Record 结构，作为模型数据输入
    :param examples:  样本
    :param label_list:标签list
    :param max_seq_length: 预先设定的最大序列长度
    :param tokenizer: tokenizer 对象
    :param output_file: tf.record 输出路径
    :param mode:
    :return:
    """
    writer = tf.python_io.TFRecordWriter(output_file)
    # 遍历训练数据
    for (ex_index, example) in enumerate(examples):
        if ex_index % 5000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
        # 对于每一个训练样本,
        feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer, mode)

        def create_int_feature(values):
            f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
            return f

        features = collections.OrderedDict()
        features["input_ids"] = create_int_feature(feature.input_ids)
        features["input_mask"] = create_int_feature(feature.input_mask)
        features["segment_ids"] = create_int_feature(feature.segment_ids)
        features["label_ids"] = create_int_feature(feature.label_ids)
        # features["label_mask"] = create_int_feature(feature.label_mask)
        # tf.train.Example/Feature 是一种协议，方便序列化？？？
        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(tf_example.SerializeToString())


def file_based_input_fn_builder(input_file, seq_length, is_training, drop_remainder):
    name_to_features = {
        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
        "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "label_ids": tf.FixedLenFeature([seq_length], tf.int64),
        # "label_ids":tf.VarLenFeature(tf.int64),
        # "label_mask": tf.FixedLenFeature([seq_length], tf.int64),
    }

    def _decode_record(record, name_to_features):
        example = tf.parse_single_example(record, name_to_features)
        for name in list(example.keys()):
            t = example[name]
            if t.dtype == tf.int64:
                t = tf.to_int32(t)
            example[name] = t
        return example

    def input_fn(params):
        batch_size = params["batch_size"]
        d = tf.data.TFRecordDataset(input_file)
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)
        d = d.apply(tf.contrib.data.map_and_batch(
            lambda record: _decode_record(record, name_to_features),
            batch_size=batch_size,
            drop_remainder=drop_remainder
        ))
        return d

    return input_fn


def create_model(bert_config, is_training, input_ids, input_mask,
                 segment_ids, labels, num_labels, use_one_hot_embeddings):
    """
    创建X模型
    :param bert_config: bert 配置
    :param is_training:
    :param input_ids: 数据的idx 表示
    :param input_mask:
    :param segment_ids:
    :param labels: 标签的idx 表示
    :param num_labels: 类别数量
    :param use_one_hot_embeddings:
    :return:
    """
    # 使用数据加载BertModel,获取对应的字embedding
    model = modeling.BertModel(
        config=bert_config,
        is_training=is_training,
        input_ids=input_ids,
        input_mask=input_mask,
        token_type_ids=segment_ids,
        use_one_hot_embeddings=use_one_hot_embeddings
    )
    # 获取对应的embedding 输入数据[batch_size, seq_length, embedding_size]
    embedding = model.get_sequence_output()
    max_seq_length = embedding.shape[1].value

    used = tf.sign(tf.abs(input_ids))
    lengths = tf.reduce_sum(used, reduction_indices=1)  # [batch_size] 大小的向量，包含了当前batch中的序列长度

    blstm_crf = BLSTM_CRF(embedded_chars=embedding, hidden_unit=FLAGS.lstm_size, cell_type=FLAGS.cell, num_layers=FLAGS.num_layers,
                          dropout_rate=FLAGS.droupout_rate, initializers=initializers, num_labels=num_labels,
                          seq_length=max_seq_length, labels=labels, lengths=lengths, is_training=is_training)
    rst = blstm_crf.add_blstm_crf_layer(crf_only=False)
    return rst


def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
    """
    构建模型
    :param bert_config:
    :param num_labels:
    :param init_checkpoint:
    :param learning_rate:
    :param num_train_steps:
    :param num_warmup_steps:
    :param use_tpu:
    :param use_one_hot_embeddings:
    :return:
    """

    def model_fn(features, labels, mode, params):
        tf.logging.info("*** Features ***")
        for name in sorted(features.keys()):
            tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))
        input_ids = features["input_ids"]
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]
        label_ids = features["label_ids"]

        print('shape of input_ids', input_ids.shape)
        # label_mask = features["label_mask"]
        is_training = (mode == tf.estimator.ModeKeys.TRAIN)

        # 使用参数构建模型,input_idx 就是输入的样本idx表示，label_ids 就是标签的idx表示
        (total_loss, logits, trans, pred_ids) = create_model(
            bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
            num_labels, use_one_hot_embeddings)

        tvars = tf.trainable_variables()
        scaffold_fn = None
        # 加载BERT模型
        if init_checkpoint:
            (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,
                                                                                                       init_checkpoint)
            tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
            if use_tpu:
                def tpu_scaffold():
                    tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
                    return tf.train.Scaffold()

                scaffold_fn = tpu_scaffold
            else:
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

        tf.logging.info("**** Trainable Variables ****")

        # 打印加载模型的参数
#         for var in tvars:
#             init_string = ""
#             if var.name in initialized_variable_names:
#                 init_string = ", *INIT_FROM_CKPT*"
#             tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
#                             init_string)
        output_spec = None
        if mode == tf.estimator.ModeKeys.TRAIN:
            train_op = optimization.create_optimizer(
                total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                train_op=train_op,
                scaffold_fn=scaffold_fn)  # 钩子，这里用来将BERT中的参数作为我们模型的初始值
        elif mode == tf.estimator.ModeKeys.EVAL:
            # 针对NER ,进行了修改
            def metric_fn(label_ids, logits, trans):
                # 首先对结果进行维特比解码
                # crf 解码

                weight = tf.sequence_mask(FLAGS.max_seq_length)
                precision = tf_metrics.precision(label_ids, pred_ids, num_labels, [2, 3, 4, 5, 6, 7], weight)
                recall = tf_metrics.recall(label_ids, pred_ids, num_labels, [2, 3, 4, 5, 6, 7], weight)
                f = tf_metrics.f1(label_ids, pred_ids, num_labels, [2, 3, 4, 5, 6, 7], weight)

                return {
                    "eval_precision": precision,
                    "eval_recall": recall,
                    "eval_f": f,
                    # "eval_loss": loss,
                }

            eval_metrics = (metric_fn, [label_ids, logits, trans])
            # eval_metrics = (metric_fn, [label_ids, logits])
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                eval_metrics=eval_metrics,
                scaffold_fn=scaffold_fn)  #
        else:
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                predictions=pred_ids,
                scaffold_fn=scaffold_fn
            )
        return output_spec

    return model_fn


def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)
    processors = {
        "ner": NerProcessor
    }
#     if not FLAGS.do_train and not FLAGS.do_eval:
#         raise ValueError("At least one of `do_train` or `do_eval` must be True.")

    bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

    if FLAGS.max_seq_length > bert_config.max_position_embeddings:
        raise ValueError(
            "Cannot use sequence length %d because the BERT model "
            "was only trained up to sequence length %d" %
            (FLAGS.max_seq_length, bert_config.max_position_embeddings))

    # 在train 的时候，才删除上一轮产出的文件，在predicted 的时候不做clean
    if FLAGS.clean and FLAGS.do_train:
        if os.path.exists(FLAGS.output_dir):
            def del_file(path):
                ls = os.listdir(path)
                for i in ls:
                    c_path = os.path.join(path, i)
                    if os.path.isdir(c_path):
                        del_file(c_path)
                    else:
                        os.remove(c_path)
            try:
                del_file(FLAGS.output_dir)
            except Exception as e:
                print(e)
                print('pleace remove the files of output dir and data.conf')
                exit(-1)
        if os.path.exists(FLAGS.data_config_path):
            try:
                os.remove(FLAGS.data_config_path)
            except Exception as e:
                print(e)
                print('pleace remove the files of output dir and data.conf')
                exit(-1)
    task_name = FLAGS.task_name.lower()
    if task_name not in processors:
        raise ValueError("Task not found: %s" % (task_name))
    processor = processors[task_name]()

    label_list = processor.get_labels()

    tokenizer = tokenization.FullTokenizer(
        vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
        tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
            FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2

    run_config = tf.contrib.tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        tpu_config=tf.contrib.tpu.TPUConfig(
            iterations_per_loop=FLAGS.iterations_per_loop,
            num_shards=FLAGS.num_tpu_cores,
            per_host_input_for_training=is_per_host))

    train_examples = None
    num_train_steps = None
    num_warmup_steps = None

    if os.path.exists(FLAGS.data_config_path):
        with codecs.open(FLAGS.data_config_path) as fd:
            data_config = json.load(fd)
    else:
        data_config = {}

    if FLAGS.do_train:
        # 加载训练数据
        if len(data_config) == 0:
            train_examples = processor.get_train_examples(FLAGS.data_dir)
            num_train_steps = int(
                len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
            num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

            data_config['num_train_steps'] = num_train_steps
            data_config['num_warmup_steps'] = num_warmup_steps
            data_config['num_train_size'] = len(train_examples)
        else:
            num_train_steps = int(data_config['num_train_steps'])
            num_warmup_steps = int(data_config['num_warmup_steps'])
    # 返回的model_dn 是一个函数，其定义了模型，训练，评测方法，并且使用钩子参数，加载了BERT模型的参数进行了自己模型的参数初始化过程
    # tf 新的架构方法，通过定义model_fn 函数，定义模型，然后通过EstimatorAPI进行模型的其他工作，Es就可以控制模型的训练，预测，评估工作等。
    model_fn = model_fn_builder(
        bert_config=bert_config,
        num_labels=len(label_list) + 1,
        init_checkpoint=FLAGS.init_checkpoint,
        learning_rate=FLAGS.learning_rate,
        num_train_steps=num_train_steps,
        num_warmup_steps=num_warmup_steps,
        use_tpu=FLAGS.use_tpu,
        use_one_hot_embeddings=FLAGS.use_tpu)

    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,
        config=run_config,
        train_batch_size=FLAGS.train_batch_size,
        eval_batch_size=FLAGS.eval_batch_size,
        predict_batch_size=FLAGS.predict_batch_size)

    if FLAGS.do_train:
        # 1. 将数据转化为tf_record 数据
        if data_config.get('train.tf_record_path', '') == '':
            train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
            filed_based_convert_examples_to_features(
                train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
        else:
            train_file = data_config.get('train.tf_record_path')
        num_train_size = num_train_size = int(data_config['num_train_size'])
        tf.logging.info("***** Running training *****")
        tf.logging.info("  Num examples = %d", num_train_size)
        tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)

        # 2.读取record 数据，组成batch
        train_input_fn = file_based_input_fn_builder(
            input_file=train_file,
            seq_length=FLAGS.max_seq_length,
            is_training=True,
            drop_remainder=True)
        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
    if FLAGS.do_eval:
        if data_config.get('eval.tf_record_path', '') == '':
            eval_examples = processor.get_dev_examples(FLAGS.data_dir)
            eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
            filed_based_convert_examples_to_features(
                eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
            data_config['eval.tf_record_path'] = eval_file
            data_config['num_eval_size'] = len(eval_examples)
        else:
            eval_file = data_config['eval.tf_record_path']
        # 打印验证集数据信息
        num_eval_size = data_config.get('num_eval_size', 0)
        tf.logging.info("***** Running evaluation *****")
        tf.logging.info("  Num examples = %d", num_eval_size)
        tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)

        eval_steps = None
        if FLAGS.use_tpu:
            eval_steps = int(num_eval_size / FLAGS.eval_batch_size)
        eval_drop_remainder = True if FLAGS.use_tpu else False
        eval_input_fn = file_based_input_fn_builder(
            input_file=eval_file,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=eval_drop_remainder)
        result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
        output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
        with codecs.open(output_eval_file, "w", encoding='utf-8') as writer:
            tf.logging.info("***** Eval results *****")
            for key in sorted(result.keys()):
                tf.logging.info("  %s = %s", key, str(result[key]))
                writer.write("%s = %s\n" % (key, str(result[key])))

    # 保存数据的配置文件，避免在以后的训练过程中多次读取训练以及测试数据集，消耗时间
    if not os.path.exists(FLAGS.data_config_path):
        with codecs.open(FLAGS.data_config_path, 'a', encoding='utf-8') as fd:
            json.dump(data_config, fd)

    if FLAGS.do_predict:
        token_path = os.path.join(FLAGS.output_dir, "token_test.txt")
        if os.path.exists(token_path):
            os.remove(token_path)

        with codecs.open(os.path.join(FLAGS.output_dir, 'label2id.pkl'), 'rb') as rf:
            label2id = pickle.load(rf)
            id2label = {value: key for key, value in label2id.items()}

        predict_examples = processor.get_test_examples(FLAGS.data_dir)
        predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
        filed_based_convert_examples_to_features(predict_examples, label_list,
                                                 FLAGS.max_seq_length, tokenizer,
                                                 predict_file, mode="test")

        tf.logging.info("***** Running prediction*****")
        tf.logging.info("  Num examples = %d", len(predict_examples))
        tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)
        if FLAGS.use_tpu:
            # Warning: According to tpu_estimator.py Prediction on TPU is an
            # experimental feature and hence not supported here
            raise ValueError("Prediction in TPU not supported")
        predict_drop_remainder = True if FLAGS.use_tpu else False
        predict_input_fn = file_based_input_fn_builder(
            input_file=predict_file,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=predict_drop_remainder)

        predicted_result = estimator.evaluate(input_fn=predict_input_fn)
        output_eval_file = os.path.join(FLAGS.output_dir, "predicted_results.txt")
        with codecs.open(output_eval_file, "w", encoding='utf-8') as writer:
            tf.logging.info("***** Predict results *****")
            for key in sorted(predicted_result.keys()):
                tf.logging.info("  %s = %s", key, str(predicted_result[key]))
                writer.write("%s = %s\n" % (key, str(predicted_result[key])))

        result = estimator.predict(input_fn=predict_input_fn)
        output_predict_file = os.path.join(FLAGS.output_dir, "label_test.txt")

        def result_to_pair(writer):
            for predict_line, prediction in zip(predict_examples, result):
                idx = 0
                line = ''
                line_token = str(predict_line.text).split(' ')
                label_token = str(predict_line.label).split(' ')
                len_seq = len(label_token)
                if len(line_token) != len(label_token):
                    tf.logging.info(predict_line.text)
                    tf.logging.info(predict_line.label)
                for id in prediction:
                    if idx > len_seq:
                        break
                    if id == 0:
                        continue
                    curr_labels = id2label[id]
                    if curr_labels in ['[CLS]', '[SEP]']:
                        continue
                    # 不知道为什么，这里会出现idx out of range 的错误。。。do not know why here cache list out of range exception!
                    try:
                        line += line_token[idx] + ' ' + label_token[idx] + ' ' + curr_labels + '\n'
                    except Exception as e:
                        tf.logging.info(e)
                        tf.logging.info(predict_line.text)
                        tf.logging.info(predict_line.label)
                        line = ''
                        break
                    idx += 1
                writer.write(line + '\n')
        # 将模型预测的结果和原始标签写入到文件中，以空格分开，使用conevel.py脚本来预测entity level 的结果并且输出
        with codecs.open(output_predict_file, 'w', encoding='utf-8') as writer:
            result_to_pair(writer)
        from conlleval import return_report
        eval_result = return_report(output_predict_file)
        print(''.join(eval_result))
        with codecs.open(os.path.join(FLAGS.output_dir, 'entity_level_predicted_result.txt'), 'a', encoding='utf-8') as fd:
            fd.write(''.join(eval_result))


def load_data():
    processer = NerProcessor()
    processer.get_labels()
    example = processer.get_train_examples(FLAGS.data_dir)
    print()
if __name__ == "__main__":
#     flags.mark_flag_as_required("data_dir")
#     flags.mark_flag_as_required("task_name")
#     flags.mark_flag_as_required("vocab_file")
#     flags.mark_flag_as_required("bert_config_file")
#     flags.mark_flag_as_required("output_dir")
    # flags.FLAGS.set_default('do_train', False)
    # flags.FLAGS.set_default('do_eval', False)
    # flags.FLAGS.set_default('do_predict', True)
    tf.app.run()
    # load_data()