BILSTM+CRF实现命名实体识别NER

#第一步：数据处理
#pikle是一个将任意复杂的对象转成对象的文本或二进制表示的过程。
#同样，必须能够将对象经过序列化后的形式恢复到原有的对象。
#在 Python 中，这种序列化过程称为 pickle，
#可以将对象 pickle 成字符串、磁盘上的文件或者任何类似于文件的对象，
#也可以将这些字符串、文件或任何类似于文件的对象 unpickle 成原来的对象。
import sys, pickle, os, random
import numpy as np

## tags, BIO
tag2label = {"O": 0,
             "B-PER": 1, "I-PER": 2,
             "B-LOC": 3, "I-LOC": 4,
             "B-ORG": 5, "I-ORG": 6
             }

#输入train_data文件的路径，读取训练集的语料，输出train_data
def read_corpus(corpus_path):
    """
    read corpus and return the list of samples
    :param corpus_path:
    :return: data
    """
    data = []
    with open(corpus_path, encoding='utf-8') as fr:
        '''lines的形状为['北\tB-LOC\n','京\tI-LOC\n','的\tO\n','...']总共有2220537个字及对应的tag'''
        lines = fr.readlines()
    sent_, tag_ = [], []
    for line in lines:
        if line != '\n':
            #char 与 label之间有个空格
		     #ine.strip()的意思是去掉每句话句首句尾的空格
		     #.split()的意思是根据空格来把整句话切割成一片片独立的字符串放到数组中，同时删除句子中的换行符号\n
            [char, label] = line.strip().split()
            #把一个个的字放进sent_
            sent_.append(char)
            #把字后面的tag放进tag_
            tag_.append(label)
        else:
            data.append((sent_, tag_))
            sent_, tag_ = [], []
    """ data的形状为[(['我',在'北','京'],['O','O','B-LOC','I-LOC'])...第一句话
                     (['我',在'天','安','门'],['O','O','B-LOC','I-LOC','I-LOC'])...第二句话  
                      ( 第三句话 )  ] 总共有50658句话"""
    return data

#由train_data来构造一个(统计非重复字)字典{'第一个字':[对应的id,该字出现的次数],'第二个字':[对应的id,该字出现的次数], , ,}
#去除低频词，生成一个word_id的字典并保存在输入的vocab_path的路径下，
#保存的方法是pickle模块自带的dump方法，保存后的文件格式是word2id.pkl文件
def vocab_build(vocab_path, corpus_path, min_count):
    """

    :param vocab_path:
    :param corpus_path:
    :param min_count:
    :return:
    """
    data = read_corpus(corpus_path)
    word2id = {}
    #sent_的形状为['我',在'北','京']，对应的tag_为['O','O','B-LOC','I-LOC']
    for sent_, tag_ in data:
        for word in sent_:
            #如果字符串只包含数字则返回 True 否则返回 False。
            if word.isdigit():
                word = ''
            #A-Z：(\u0041-\u005a)    a-z ：\u0061-\u007a
            elif ('\u0041' <= word <='\u005a') or ('\u0061' <= word <='\u007a'):
                word = ''
            if word not in word2id:
                #[len(word2id)+1, 1]用来统计[位置标签，出现次数]，第一次出现定为1
                word2id[word] = [len(word2id)+1, 1]
            else:
                #word2id[word][1]实现对词频的统计，出现次数累加1
                word2id[word][1] += 1
    #用来统计低频词
    low_freq_words = []
    for word, [word_id, word_freq] in word2id.items():
        #寻找低于某个数字的低频词
        if word_freq < min_count and word != '' and word != '':
            low_freq_words.append(word)
    for word in low_freq_words:
        #把这些低频词从字典中删除
        del word2id[word]
    #删除低频词后为每个字重新建立id，而不再统计词频
    new_id = 1
    for word in word2id.keys():
        word2id[word] = new_id
        new_id += 1
    word2id[''] = new_id
    word2id[''] = 0

    print(len(word2id))
    with open(vocab_path, 'wb') as fw:
        # 序列化到名字为word2id.pkl文件
        pickle.dump(word2id, fw)
        
#通过pickle模块自带的load方法(反序列化方法)加载输出word2id
def read_dictionary(vocab_path):
    """

    :param vocab_path:
    :return:
    """
    vocab_path = os.path.join(vocab_path)
    with open(vocab_path, 'rb') as fr:
        #反序列化方法加载输出
        word2id = pickle.load(fr)
    print('vocab_size:', len(word2id))
    return word2id
'''word2id的形状为{'当': 1, '希': 2, '望': 3, '工': 4, '程': 5,。。'': 3904, '': 0}
   总共3903个字'''
   
#输入一句话，生成一个 sentence_id 
'''sentence_id的形状为[1,2,3,4,...]对应的sent为['当','希','望','工',程'...]'''
def sentence2id(sent, word2id):
    """

    :param sent:
    :param word2id:
    :return:
    """
    sentence_id = []
    for word in sent:
        if word.isdigit():
            word = ''
        elif ('\u0041' <= word <= '\u005a') or ('\u0061' <= word <= '\u007a'):
            word = ''
        #如果sent中的词在word2id找不到，用--->3905来表示
        if word not in word2id:
            word = ''
        sentence_id.append(word2id[word])
    return sentence_id


#输入vocab，vocab就是前面得到的word2id，embedding_dim=300
def random_embedding(vocab, embedding_dim):
    """

    :param vocab:
    :param embedding_dim:
    :return:
    """
    #返回一个len(vocab)*embedding_dim=3905*300的矩阵(每个字投射到300维)作为初始值
    embedding_mat = np.random.uniform(-0.25, 0.25, (len(vocab), embedding_dim))
    embedding_mat = np.float32(embedding_mat)
    return embedding_mat

#padding,输入一句话，不够标准的样本用pad_mark来补齐
''' 
输入：seqs的形状为二维矩阵，形状为[[33,12,17,88,50]-第一句话
                                 [52,19,14,48,66,31,89]-第二句话
                                                    ] 
输出：seq_list为seqs经过padding后的序列
      seq_len_list保留了padding之前每条样本的真实长度
      seq_list和seq_len_list用来喂给feed_dict
'''
def pad_sequences(sequences, pad_mark=0):
    
    '''
    :param sequences:
    :param pad_mark:
    :return:
    '''
    #返回一个序列中长度最长的那条样本的长度
    max_len = max(map(lambda x : len(x), sequences))
    seq_list, seq_len_list = [], []
    for seq in sequences:
        #由元组格式()转化为列表格式[]
        seq = list(seq)
        #不够最大长度的样本用0补上放到列表seq_list
        seq_ = seq[:max_len] + [pad_mark] * max(max_len - len(seq), 0)
        seq_list.append(seq_)
        #seq_len_list用来统计每个样本的真实长度
        seq_len_list.append(min(len(seq), max_len))
    return seq_list, seq_len_list


#生成batch
''' seqs的形状为二维矩阵，形状为[[33,12,17,88,50....]...第一句话
                                [52,19,14,48,66....]...第二句话
                                                    ] 
   labels的形状为二维矩阵，形状为[[0, 0, 3, 4]....第一句话
                                 [0, 0, 3, 4]...第二句话
                                             ]
'''   
def batch_yield(data, batch_size, vocab, tag2label, shuffle=False):
    """

    :param data:
    :param batch_size:
    :param vocab:
    :param tag2label:
    :param shuffle:
    :return:
    """
    if shuffle:
        random.shuffle(data)

    seqs, labels = [], []
    for (sent_, tag_) in data:
        #sent_的形状为[33,12,17,88,50....]句中的字在Wordid对应的位置标签
        #如果tag_形状为['O','O','B-LOC','I-LOC']，对应的label_形状为[0, 0, 3, 4]
        #返回tag2label字典中每个tag对应的value值
        sent_ = sentence2id(sent_, vocab)
        label_ = [tag2label[tag] for tag in tag_]
       #保证了seqs的长度为batch_size
        if len(seqs) == batch_size:
            yield seqs, labels
            seqs, labels = [], []
        
        seqs.append(sent_)
        labels.append(label_)

    if len(seqs) != 0:
        yield seqs, labels

#第二步:设置模型
import numpy as np
import os, time, sys
import tensorflow as tf
from tensorflow.contrib.rnn import LSTMCell
from tensorflow.contrib.crf import crf_log_likelihood
from tensorflow.contrib.crf import viterbi_decode
from data import pad_sequences, batch_yield
from utils import get_logger
from eval import conlleval


class BiLSTM_CRF(object):
    def __init__(self, args, embeddings, tag2label, vocab, paths, config):
        self.batch_size = args.batch_size
        self.epoch_num = args.epoch
        self.hidden_dim = args.hidden_dim
        self.embeddings = embeddings
        self.CRF = args.CRF
        self.update_embedding = args.update_embedding
        self.dropout_keep_prob = args.dropout
        self.optimizer = args.optimizer
        self.lr = args.lr
        self.clip_grad = args.clip
        self.tag2label = tag2label
        self.num_tags = len(tag2label)
        self.vocab = vocab
        self.shuffle = args.shuffle
        self.model_path = paths['model_path']
        self.summary_path = paths['summary_path']
        self.logger = get_logger(paths['log_path'])
        self.result_path = paths['result_path']
        self.config = config

    def build_graph(self):
        self.add_placeholders()
        self.lookup_layer_op()
        self.biLSTM_layer_op()
        self.softmax_pred_op()
        self.loss_op()
        self.trainstep_op()
        self.init_op()

    def add_placeholders(self):
        self.word_ids = tf.placeholder(tf.int32, shape=[None, None], name="word_ids")
        self.labels = tf.placeholder(tf.int32, shape=[None, None], name="labels")
        self.sequence_lengths = tf.placeholder(tf.int32, shape=[None], name="sequence_lengths")

        self.dropout_pl = tf.placeholder(dtype=tf.float32, shape=[], name="dropout")
        self.lr_pl = tf.placeholder(dtype=tf.float32, shape=[], name="lr")

    def lookup_layer_op(self):
        with tf.variable_scope("words"):
            _word_embeddings = tf.Variable(self.embeddings,
                                           dtype=tf.float32,
                                           trainable=self.update_embedding,
                                           name="_word_embeddings")
            word_embeddings = tf.nn.embedding_lookup(params=_word_embeddings,
                                                     ids=self.word_ids,
                                                     name="word_embeddings")
        self.word_embeddings =  tf.nn.dropout(word_embeddings, self.dropout_pl)

    def biLSTM_layer_op(self):
        with tf.variable_scope("bi-lstm"):
            cell_fw = LSTMCell(self.hidden_dim)
            cell_bw = LSTMCell(self.hidden_dim)
            (output_fw_seq, output_bw_seq), _ = tf.nn.bidirectional_dynamic_rnn(
                cell_fw=cell_fw,
                cell_bw=cell_bw,
                inputs=self.word_embeddings,
                sequence_length=self.sequence_lengths,
                dtype=tf.float32)
            #维持行数不变，后面的行接到前面的行后面
            output = tf.concat([output_fw_seq, output_bw_seq], axis=-1)
            #经过droupput处理
            output = tf.nn.dropout(output, self.dropout_pl)

        with tf.variable_scope("proj"):
            W = tf.get_variable(name="W",
                                shape=[2 * self.hidden_dim, self.num_tags],
                                #该函数返回一个用于初始化权重的初始化程序 “Xavier” 。
                                #这个初始化器是用来保持每一层的梯度大小都差不多相同
                                initializer=tf.contrib.layers.xavier_initializer(),
                                dtype=tf.float32)

            b = tf.get_variable(name="b",
                                shape=[self.num_tags],
                                #tf.zeros_initializer()，也可以简写为tf.Zeros()
                                initializer=tf.zeros_initializer(),
                                dtype=tf.float32)
            #output的形状为[batch_size,steps,cell_num]
            s = tf.shape(output)
            #reshape的目的是为了跟w做矩阵乘法
            output = tf.reshape(output, [-1, 2*self.hidden_dim])
            pred = tf.matmul(output, W) + b
            #s[1]=batch_size
            self.logits = tf.reshape(pred, [-1, s[1], self.num_tags])

    def loss_op(self):
        if self.CRF:
            #crf_log_likelihood作为损失函数
            #inputs：unary potentials,就是每个标签的预测概率值
            #tag_indices，这个就是真实的标签序列了
            #sequence_lengths,一个样本真实的序列长度，为了对齐长度会做些padding，但是可以把真实的长度放到这个参数里
            #transition_params,转移概率，可以没有，没有的话这个函数也会算出来
            #输出：log_likelihood:标量;transition_params,转移概率，如果输入没输，它就自己算个给返回

            log_likelihood, self.transition_params = crf_log_likelihood(inputs=self.logits,
                                                                   tag_indices=self.labels,
                                                                   sequence_lengths=self.sequence_lengths)
            self.loss = -tf.reduce_mean(log_likelihood)

        else:
            #交叉熵做损失函数
            losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits,
                                                                    labels=self.labels)
            mask = tf.sequence_mask(self.sequence_lengths)
            losses = tf.boolean_mask(losses, mask)
            self.loss = tf.reduce_mean(losses)
        #添加标量统计结果
        tf.summary.scalar("loss", self.loss)

    def softmax_pred_op(self):
        if not self.CRF:
            self.labels_softmax_ = tf.argmax(self.logits, axis=-1)
            self.labels_softmax_ = tf.cast(self.labels_softmax_, tf.int32)

    def trainstep_op(self):
        with tf.variable_scope("train_step"):
            self.global_step = tf.Variable(0, name="global_step", trainable=False)
            if self.optimizer == 'Adam':
                optim = tf.train.AdamOptimizer(learning_rate=self.lr_pl)
            elif self.optimizer == 'Adadelta':
                optim = tf.train.AdadeltaOptimizer(learning_rate=self.lr_pl)
            elif self.optimizer == 'Adagrad':
                optim = tf.train.AdagradOptimizer(learning_rate=self.lr_pl)
            elif self.optimizer == 'RMSProp':
                optim = tf.train.RMSPropOptimizer(learning_rate=self.lr_pl)
            elif self.optimizer == 'Momentum':
                optim = tf.train.MomentumOptimizer(learning_rate=self.lr_pl, momentum=0.9)
            elif self.optimizer == 'SGD':
                optim = tf.train.GradientDescentOptimizer(learning_rate=self.lr_pl)
            else:
                optim = tf.train.GradientDescentOptimizer(learning_rate=self.lr_pl)

            grads_and_vars = optim.compute_gradients(self.loss)
            grads_and_vars_clip = [[tf.clip_by_value(g, -self.clip_grad, self.clip_grad), v] for g, v in grads_and_vars]
            self.train_op = optim.apply_gradients(grads_and_vars_clip, global_step=self.global_step)

    def init_op(self):
        self.init_op = tf.global_variables_initializer()

    def add_summary(self, sess):
        """

        :param sess:
        :return:
        """
        self.merged = tf.summary.merge_all()
        self.file_writer = tf.summary.FileWriter(self.summary_path, sess.graph)

    def train(self, train, dev):
        """

        :param train:
        :param dev:
        :return:
        """
        saver = tf.train.Saver(tf.global_variables())

        with tf.Session(config=self.config) as sess:
            sess.run(self.init_op)
            self.add_summary(sess)
            #epoch_num=40
            for epoch in range(self.epoch_num):
                self.run_one_epoch(sess, train, dev, self.tag2label, epoch, saver)

    def test(self, test):
        saver = tf.train.Saver()
        with tf.Session(config=self.config) as sess:
            self.logger.info('=========== testing ===========')
            saver.restore(sess, self.model_path)
            label_list, seq_len_list = self.dev_one_epoch(sess, test)
            self.evaluate(label_list, seq_len_list, test)

    def demo_one(self, sess, sent):
        """

        :param sess:
        :param sent: 
        :return:
        """
        label_list = []
        for seqs, labels in batch_yield(sent, self.batch_size, self.vocab, self.tag2label, shuffle=False):
            label_list_, _ = self.predict_one_batch(sess, seqs)
            label_list.extend(label_list_)
        label2tag = {}
        for tag, label in self.tag2label.items():
            label2tag[label] = tag if label != 0 else label
        tag = [label2tag[label] for label in label_list[0]]
        return tag

    def run_one_epoch(self, sess, train, dev, tag2label, epoch, saver):
        """

        :param sess:
        :param train:
        :param dev:
        :param tag2label:
        :param epoch:
        :param saver:
        :return:
        """
        #计算出多少个batch，计算过程：(50658+64-1)//64=792
        num_batches = (len(train) + self.batch_size - 1) // self.batch_size
        #记录开始训练的时间
        start_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
        #产生每一个batch
        batches = batch_yield(train, self.batch_size, self.vocab, self.tag2label, shuffle=self.shuffle)
        for step, (seqs, labels) in enumerate(batches):
            #sys.stdout 是标准输出文件，write就是往这个文件写数据
            sys.stdout.write(' processing: {} batch / {} batches.'.format(step + 1, num_batches) + '\r')
            #step_num=epoch*792+step+1
            step_num = epoch * num_batches + step + 1
            feed_dict, _ = self.get_feed_dict(seqs, labels, self.lr, self.dropout_keep_prob)
            _, loss_train, summary, step_num_ = sess.run([self.train_op, self.loss, self.merged, self.global_step],
                                                         feed_dict=feed_dict)
            if step + 1 == 1 or (step + 1) % 300 == 0 or step + 1 == num_batches:
                self.logger.info(
                    '{} epoch {}, step {}, loss: {:.4}, global_step: {}'.format(start_time, epoch + 1, step + 1,
                                                                                loss_train, step_num))

            self.file_writer.add_summary(summary, step_num)

            if step + 1 == num_batches:
                #训练的最后一个batch保存模型
                saver.save(sess, self.model_path, global_step=step_num)

        self.logger.info('===========validation / test===========')
        label_list_dev, seq_len_list_dev = self.dev_one_epoch(sess, dev)
        self.evaluate(label_list_dev, seq_len_list_dev, dev, epoch)

    def get_feed_dict(self, seqs, labels=None, lr=None, dropout=None):
        """

        :param seqs:
        :param labels:
        :param lr:
        :param dropout:
        :return: feed_dict
        """
        #seq_len_list用来统计每个样本的真实长度
        #word_ids就是seq_list，padding后的样本序列
        word_ids, seq_len_list = pad_sequences(seqs, pad_mark=0)

        feed_dict = {self.word_ids: word_ids,
                     self.sequence_lengths: seq_len_list}
        if labels is not None:
            #labels经过padding后，喂给feed_dict
            labels_, _ = pad_sequences(labels, pad_mark=0)
            feed_dict[self.labels] = labels_
        if lr is not None:
            feed_dict[self.lr_pl] = lr
        if dropout is not None:
            feed_dict[self.dropout_pl] = dropout

        #seq_len_list用来统计每个样本的真实长度
        return feed_dict, seq_len_list

    def dev_one_epoch(self, sess, dev):
        """

        :param sess:
        :param dev:
        :return:
        """
        label_list, seq_len_list = [], []
        for seqs, labels in batch_yield(dev, self.batch_size, self.vocab, self.tag2label, shuffle=False):
            label_list_, seq_len_list_ = self.predict_one_batch(sess, seqs)
            label_list.extend(label_list_)
            seq_len_list.extend(seq_len_list_)
        return label_list, seq_len_list

    def predict_one_batch(self, sess, seqs):
        """

        :param sess:
        :param seqs:
        :return: label_list
                 seq_len_list
        """
        #seq_len_list用来统计每个样本的真实长度
        feed_dict, seq_len_list = self.get_feed_dict(seqs, dropout=1.0)

        if self.CRF:
            #transition_params代表转移概率，由crf_log_likelihood方法计算出
            logits, transition_params = sess.run([self.logits, self.transition_params],
                                                 feed_dict=feed_dict)
            label_list = []
            # 打包成元素形式为元组的列表[(logit,seq_len),(logit,seq_len),( ,),]
            for logit, seq_len in zip(logits, seq_len_list):
                viterbi_seq, _ = viterbi_decode(logit[:seq_len], transition_params)
                label_list.append(viterbi_seq)
            return label_list, seq_len_list

        else:
            label_list = sess.run(self.labels_softmax_, feed_dict=feed_dict)
            return label_list, seq_len_list

    def evaluate(self, label_list, seq_len_list, data, epoch=None):
        """

        :param label_list:
        :param seq_len_list:
        :param data:
        :param epoch:
        :return:
        """
        label2tag = {}
        for tag, label in self.tag2label.items():
            label2tag[label] = tag if label != 0 else label

        model_predict = []
        for label_, (sent, tag) in zip(label_list, data):
            tag_ = [label2tag[label__] for label__ in label_]
            sent_res = []
            if  len(label_) != len(sent):
                print(sent)
                print(len(label_))
                print(tag)
            for i in range(len(sent)):
                sent_res.append([sent[i], tag[i], tag_[i]])
            model_predict.append(sent_res)
        epoch_num = str(epoch+1) if epoch != None else 'test'
        label_path = os.path.join(self.result_path, 'label_' + epoch_num)
        metric_path = os.path.join(self.result_path, 'result_metric_' + epoch_num)
        for _ in conlleval(model_predict, label_path, metric_path):
            self.logger.info(_)

#第三步
import logging, sys, argparse


def str2bool(v):
    # copy from StackOverflow
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        #首先被内层IOError异常捕获，打印“inner exception”, 然后把相同的异常再抛出，
        #被外层的except捕获，打印"outter exception"
        raise argparse.ArgumentTypeError('Boolean value expected.')

#根据输入的tag返回对应的字符
def get_entity(tag_seq, char_seq):
    PER = get_PER_entity(tag_seq, char_seq)
    LOC = get_LOC_entity(tag_seq, char_seq)
    ORG = get_ORG_entity(tag_seq, char_seq)
    return PER, LOC, ORG

#输出PER对应的字符
def get_PER_entity(tag_seq, char_seq):
    length = len(char_seq)
    PER = []
    #构成一个zip对象,形状类似[( 1, ),( 1, ),( 2, ),( 2, )]
    #zip函数可以接受一系列的可迭代对象作为参数，将对象中对应的元素打包成一个个tuple(元组)，
    #在zip函数的括号里面加上*号，则是zip函数的逆操作
    for i, (char, tag) in enumerate(zip(char_seq, tag_seq)):
        #tag里包含了O,B-PER,I-PER,B-LOCI-PER,B-ORG,I-PER
        if tag == 'B-PER':
            if 'per' in locals().keys():
                PER.append('per')
                del per
            per = char
            if i+1 == length:
                PER.append(per)
        if tag == 'I-PER':
            per += char
            if i+1 == length:
                PER.append(per)
        if tag not in ['I-PER', 'B-PER']:
            if 'per' in locals().keys():
                PER.append(per)
                del per
            continue
    return PER

#输出LOC对应的字符
def get_LOC_entity(tag_seq, char_seq):
    length = len(char_seq)
    LOC = []
    for i, (char, tag) in enumerate(zip(char_seq, tag_seq)):
        if tag == 'B-LOC':
            if 'loc' in locals().keys():
                LOC.append('loc')
                del loc
            loc = char
            if i+1 == length:
                LOC.append(loc)
        if tag == 'I-LOC':
            loc += char
            if i+1 == length:
                LOC.append(loc)
        if tag not in ['I-LOC', 'B-LOC']:
            if 'loc' in locals().keys():
                LOC.append(loc)
                del loc
            continue
    return LOC

#输出ORG对应的字符
def get_ORG_entity(tag_seq, char_seq):
    length = len(char_seq)
    ORG = []
    for i, (char, tag) in enumerate(zip(char_seq, tag_seq)):
        if tag == 'B-ORG':
            if 'org' in locals().keys():
                ORG.append('org')
                del org
            org = char
            if i+1 == length:
                ORG.append(org)
        if tag == 'I-ORG':
            org += char
            if i+1 == length:
                ORG.append(org)
        if tag not in ['I-ORG', 'B-ORG']:
            if 'org' in locals().keys():
                ORG.append(org)
                del org
            continue
    return ORG

#记录日志
def get_logger(filename):
    logger = logging.getLogger('logger')
    logger.setLevel(logging.DEBUG)
    logging.basicConfig(format='%(message)s', level=logging.DEBUG)
    handler = logging.FileHandler(filename)
    handler.setLevel(logging.DEBUG)
    handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s'))
    logging.getLogger().addHandler(handler)
    return logger

#第四步
import os

#使用conlleval.pl对CRF测试结果进行评价的方法
def conlleval(label_predict, label_path, metric_path):
    """

    :param label_predict:
    :param label_path:
    :param metric_path:
    :return:
    """
    eval_perl = "./conlleval_rev.pl"
    with open(label_path, "w") as fw:
        line = []
        for sent_result in label_predict:
            for char, tag, tag_ in sent_result:
                tag = '0' if tag == 'O' else tag
                char = char.encode("utf-8")
                line.append("{} {} {}\n".format(char, tag, tag_))
            line.append("\n")
        fw.writelines(line)
    os.system("perl {} < {} > {}".format(eval_perl, label_path, metric_path))
    with open(metric_path) as fr:
        metrics = [line.strip() for line in fr]
    return metrics
    

#第五步运行
import tensorflow as tf
import numpy as np
##os模块就是对操作系统进行操作
import os, argparse, time, random
from model import BiLSTM_CRF
from utils import str2bool, get_logger, get_entity
from data import read_corpus, read_dictionary, tag2label, random_embedding
## Session configuration
#在python代码中设置使用的GPU
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
#log 日志级别设置，只显示 warning 和 Error，'1' 是默认的显示等级，显示所有信息
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # default: 0

#记录设备指派情况:tf.ConfigProto(log_device_placement=True)
#设置tf.ConfigProto()中参数log_device_placement = True ,
#可以获取到 operations 和 Tensor 被指派到哪个设备(几号CPU或几号GPU)上运行,
#会在终端打印出各项操作是在哪个设备上运行的。
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.2  # need ~700MB GPU memory

## hyperparameters超参数设置
#使用argparse的第一步就是创建一个解析器对象，并告诉它将会有些什么参数。
#那么当你的程序运行时，该解析器就可以用于处理命令行参数
parser = argparse.ArgumentParser(description='BiLSTM-CRF for Chinese NER task')
parser.add_argument('--train_data', type=str, default='data_path', help='train data source')
parser.add_argument('--test_data', type=str, default='data_path', help='test data source')
parser.add_argument('--batch_size', type=int, default=64, help='#sample of each minibatch')
parser.add_argument('--epoch', type=int, default=40, help='#epoch of training')
parser.add_argument('--hidden_dim', type=int, default=300, help='#dim of hidden state')
parser.add_argument('--optimizer', type=str, default='Adam', help='Adam/Adadelta/Adagrad/RMSProp/Momentum/SGD')
parser.add_argument('--CRF', type=str2bool, default=True, help='use CRF at the top layer. if False, use Softmax')
parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
parser.add_argument('--clip', type=float, default=5.0, help='gradient clipping')
parser.add_argument('--dropout', type=float, default=0.5, help='dropout keep_prob')
parser.add_argument('--update_embedding', type=str2bool, default=True, help='update embedding during training')
parser.add_argument('--pretrain_embedding', type=str, default='random', help='use pretrained char embedding or init it randomly')
parser.add_argument('--embedding_dim', type=int, default=300, help='random init char embedding_dim')
parser.add_argument('--shuffle', type=str2bool, default=True, help='shuffle training data before each epoch')
parser.add_argument('--mode', type=str, default='demo', help='train/test/demo')
parser.add_argument('--demo_model', type=str, default='1521112368', help='model for test and demo')
#传递参数送入模型中
args = parser.parse_args()


#get char embeddings
'''word2id的形状为{'当': 1, '希': 2, '望': 3, '工': 4, '程': 5,。。'': 3904, '': 0}
   train_data总共3903个去重后的字'''
word2id = read_dictionary(os.path.join('.', args.train_data, 'word2id.pkl'))

#通过调用random_embedding函数返回一个len(vocab)*embedding_dim=3905*300的矩阵(矩阵元素均在-0.25到0.25之间)作为初始值
if args.pretrain_embedding == 'random':
    embeddings = random_embedding(word2id, args.embedding_dim)
else:
    embedding_path = 'pretrain_embedding.npy'
    embeddings = np.array(np.load(embedding_path), dtype='float32')

# read corpus and get training data
if args.mode != 'demo':
    #设置train_path的路径为data_path下的train_data文件
    train_path = os.path.join('.', args.train_data, 'train_data')
    #设置test_path的路径为data_path下的test_path文件
    test_path = os.path.join('.', args.test_data, 'test_data')
    #通过read_corpus函数读取出train_data
    """ train_data的形状为[(['我',在'北','京'],['O','O','B-LOC','I-LOC'])...第一句话
                     (['我',在'天','安','门'],['O','O','B-LOC','I-LOC','I-LOC'])...第二句话  
                      ( 第三句话 )  ] 总共有50658句话"""
    train_data = read_corpus(train_path)
    test_data = read_corpus(test_path); test_size = len(test_data)

## paths setting
paths = {}
# 时间戳就是一个时间点，一般就是为了在同步更新的情况下提高效率之用。
#就比如一个文件，如果他没有被更改，那么他的时间戳就不会改变，那么就没有必要写回，以提高效率，
#如果不论有没有被更改都重新写回的话，很显然效率会有所下降。
timestamp = str(int(time.time())) if args.mode == 'train' else args.demo_model
#输出路径output_path路径设置为data_path_save下的具体时间名字为文件名
output_path = os.path.join('.', args.train_data+"_save", timestamp)
if not os.path.exists(output_path): os.makedirs(output_path)
#summary_path的路径设置为output_path下的summaries文件
summary_path = os.path.join(output_path, "summaries")
paths['summary_path'] = summary_path
if not os.path.exists(summary_path): os.makedirs(summary_path)
#model_path的路径设置为output_path下的checkpoints文件
model_path = os.path.join(output_path, "checkpoints/")
if not os.path.exists(model_path): os.makedirs(model_path)
#ckpt_prefix保存在checkpoints下的名为model的文件
ckpt_prefix = os.path.join(model_path, "model")
paths['model_path'] = ckpt_prefix
#result_path的路径为时间戳文件下的results文件
result_path = os.path.join(output_path, "results")
paths['result_path'] = result_path
if not os.path.exists(result_path): os.makedirs(result_path)
#log_path='/results/log.txt'
log_path = os.path.join(result_path, "log.txt")
paths['log_path'] = log_path
get_logger(log_path).info(str(args))


## training model
if args.mode == 'train':
    model = BiLSTM_CRF(args, embeddings, tag2label, word2id, paths, config=config)
    #创建节点，无返回值
    model.build_graph()

    ## hyperparameters-tuning, split train/dev
    # dev_data = train_data[:5000]; dev_size = len(dev_data)
    # train_data = train_data[5000:]; train_size = len(train_data)
    # print("train data: {0}\ndev data: {1}".format(train_size, dev_size))
    # model.train(train=train_data, dev=dev_data)
    ## train model on the whole training data
    
    print("train data: {}".format(len(train_data)))
     # use test_data as the dev_data to see overfitting phenomena
    model.train(train=train_data, dev=test_data) 

## testing model
elif args.mode == 'test':
    ckpt_file = tf.train.latest_checkpoint(model_path)
    print(ckpt_file)
    paths['model_path'] = ckpt_file
    model = BiLSTM_CRF(args, embeddings, tag2label, word2id, paths, config=config)
    model.build_graph()
    print("test data: {}".format(test_size))
    model.test(test_data)

## demo
elif args.mode == 'demo':
    ckpt_file = tf.train.latest_checkpoint(model_path)
    print(ckpt_file)
    paths['model_path'] = ckpt_file
    model = BiLSTM_CRF(args, embeddings, tag2label, word2id, paths, config=config)
    model.build_graph()
    saver = tf.train.Saver()
    with tf.Session(config=config) as sess:
        print('============= demo =============')
        saver.restore(sess, ckpt_file)
        #等价于while True
        while(1):
            print('Please input your sentence:')
            #input() 函数接受一个标准输入数据，返回为 string 类型，'我是中国人'
            demo_sent = input()
            #判断输入是否为空
            if demo_sent == '' or demo_sent.isspace():
                print('See you next time!')
                break
            else:
                #去除首尾空格
                demo_sent = list(demo_sent.strip())
                #[(['我', '是', '中', '国', '人'], ['O', 'O', 'O', 'O', 'O'])]
                demo_data = [(demo_sent, ['O'] * len(demo_sent))]
                #送入模型训练，返回每个字正确的tag['O', 'O', 'B-LOC', 'I-LOC', 'O']
                tag = model.demo_one(sess, demo_data)
                #根据模型计算得到的tag，输出该tag对应的字符，比如LOC：中国
                PER, LOC, ORG = get_entity(tag, demo_sent)
                print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))

 

 输出结果:

Please input your sentence:

崔永元早些年向红十字会捐过钱
PER: ['崔永元']
LOC: []
ORG: ['红十字会']

Please input your sentence:

蔡依林在台北的时候追求过周杰伦
PER: ['蔡依林', '周杰伦']
LOC: ['台北']
ORG: []

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