Python BiLSTM_CRF医学文本标注，医学命名实体识别，NER，双向长短记忆神经网络和条件随机场应用实例，BiLSTM_CRF实现代码

具体参考的哪一位大神的代码记不清了，在此表示感谢一下：

进入正题

import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim

1.设置随机种子 

torch.manual_seed(1)

2.torch.max(input, dim) 函数

output = torch.max(input, dim)

输入

• input是softmax函数输出的一个tensor
• dim是max函数索引的维度0/1，0是每列的最大值，1是每行的最大值

输出

• 函数会返回两个tensor，第一个tensor是每行的最大值，softmax的输出中最大的是1，所以第一个tensor是全1的tensor；第二个tensor是每行最大值的索引。

def argmax(vec):
    _,idx = torch.max(vec,1)
    return idx.item()

3.将 idxs（位置下标）的列表转化为tensor格式

def prepare_sequence(seq, to_ix):
    idxs = [to_ix[w] for w in seq]
    return torch.tensor(idxs, dtype=torch.long)

4. LogSumExp(LSE)技巧，主要解决计算Softmax或CrossEntropy2时出现的上溢(overflow)或下溢(underflow)问题。

 LSE被定义为参数指数之和的对数：

输入可以看成是一个n维的向量，输出是一个标量。 

def log_sum_exp(vec):
    max_score = vec[0, argmax(vec)]
    max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
    return max_score + \
        torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))

5.双向长短记忆神经网络和条件随机场 

class BiLSTM_CRF(nn.Module):

    def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
        super(BiLSTM_CRF, self).__init__()
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        self.vocab_size = vocab_size
        self.tag_to_ix = tag_to_ix
        self.tagset_size = len(tag_to_ix)

        self.word_embeds = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,
                            num_layers=1, bidirectional=True)

        # Maps the output of the LSTM into tag space.
        # 将 LSTM 的输出映射到标签空间。
        self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)

        # Matrix of transition parameters.  Entry i,j is the score of
        # transitioning *to* i *from* j.
        # 转换参数矩阵。 条目 i,j 是转换 *to* i *from* j.
        self.transitions = nn.Parameter(
            torch.randn(self.tagset_size, self.tagset_size))

        # These two statements enforce the constraint that we never transfer
        # to the start tag and we never transfer from the stop tag
        # 这两个语句强制我们从不转移到开始标签并且我们从不从停止标签转移的约束
        self.transitions.data[tag_to_ix[START_TAG], :] = -10000
        self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000

        self.hidden = self.init_hidden()

    def init_hidden(self):
        return (torch.randn(2, 1, self.hidden_dim // 2),
                torch.randn(2, 1, self.hidden_dim // 2))

    def _forward_alg(self, feats):
        # Do the forward algorithm to compute the partition function
        init_alphas = torch.full((1, self.tagset_size), -10000.)
        # START_TAG has all of the score.
        init_alphas[0][self.tag_to_ix[START_TAG]] = 0.

        # Wrap in a variable so that we will get automatic backprop
        forward_var = init_alphas

        # Iterate through the sentence
        for feat in feats:
            alphas_t = []  # The forward tensors at this timestep
            for next_tag in range(self.tagset_size):
                # broadcast the emission score: it is the same regardless of
                # the previous tag
                emit_score = feat[next_tag].view(
                    1, -1).expand(1, self.tagset_size)
                # the ith entry of trans_score is the score of transitioning to
                # next_tag from i
                trans_score = self.transitions[next_tag].view(1, -1)
                # The ith entry of next_tag_var is the value for the
                # edge (i -> next_tag) before we do log-sum-exp
                next_tag_var = forward_var + trans_score + emit_score
                # The forward variable for this tag is log-sum-exp of all the
                # scores.
                alphas_t.append(log_sum_exp(next_tag_var).view(1))
            forward_var = torch.cat(alphas_t).view(1, -1)
        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
        alpha = log_sum_exp(terminal_var)
        return alpha

    def _get_lstm_features(self, sentence):
        self.hidden = self.init_hidden()
        embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)
        lstm_out, self.hidden = self.lstm(embeds, self.hidden)
        lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
        lstm_feats = self.hidden2tag(lstm_out)
        return lstm_feats

    def _score_sentence(self, feats, tags):
        # Gives the score of a provided tag sequence
        score = torch.zeros(1)
        tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])
        for i, feat in enumerate(feats):
            score = score + \
                self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]
        score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]
        return score

    def _viterbi_decode(self, feats):
        backpointers = []

        # Initialize the viterbi variables in log space
        init_vvars = torch.full((1, self.tagset_size), -10000.)
        init_vvars[0][self.tag_to_ix[START_TAG]] = 0

        # forward_var at step i holds the viterbi variables for step i-1
        forward_var = init_vvars
        for feat in feats:
            bptrs_t = []  # holds the backpointers for this step
            viterbivars_t = []  # holds the viterbi variables for this step

            for next_tag in range(self.tagset_size):
                # next_tag_var[i] holds the viterbi variable for tag i at the
                # previous step, plus the score of transitioning
                # from tag i to next_tag.
                # We don't include the emission scores here because the max
                # does not depend on them (we add them in below)
                next_tag_var = forward_var + self.transitions[next_tag]
                best_tag_id = argmax(next_tag_var)
                bptrs_t.append(best_tag_id)
                viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))
            # Now add in the emission scores, and assign forward_var to the set
            # of viterbi variables we just computed
            forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
            backpointers.append(bptrs_t)

        # Transition to STOP_TAG
        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
        best_tag_id = argmax(terminal_var)
        path_score = terminal_var[0][best_tag_id]

        # Follow the back pointers to decode the best path.
        best_path = [best_tag_id]
        for bptrs_t in reversed(backpointers):
            best_tag_id = bptrs_t[best_tag_id]
            best_path.append(best_tag_id)
        # Pop off the start tag (we dont want to return that to the caller)
        start = best_path.pop()
        assert start == self.tag_to_ix[START_TAG]  # Sanity check
        best_path.reverse()
        return path_score, best_path

    def neg_log_likelihood(self, sentence, tags):
        feats = self._get_lstm_features(sentence)
        forward_score = self._forward_alg(feats)
        gold_score = self._score_sentence(feats, tags)
        return forward_score - gold_score

    def forward(self, sentence):  # dont confuse this with _forward_alg above.
        # Get the emission scores from the BiLSTM
        lstm_feats = self._get_lstm_features(sentence)

        # Find the best path, given the features.
        score, tag_seq = self._viterbi_decode(lstm_feats)
        # return score, tag_seq
        return tag_seq

 6.基础参数设置：标注的起始标签和结束标签，词向量的维度（5）和隐藏层（4）设置

START_TAG = ""
STOP_TAG = ""
EMBEDDING_DIM = 5
HIDDEN_DIM = 4

7.序列标注的实例，示例就是两句医学文本，采用的BIO标注方法，B表示实体名词的起始位置，I表示实体名词的非起始位置的字符，O表示其它字符，这是最简单的标注样例。还有比较复杂的标注样例，如下所示： 

# Make up some training data
training_data = [(
    "高 血 压 容 易 造 成 心 脏 病".split(),
    "B I I O O O O B I I".split()
), (
    "心 脏 病 易 造 成 心 肌 梗 死".split(),
    "B I I O O O B I I I".split()
)]
print(training_data)

8.数据清洗过程， 建立字典-id

word_to_ix = {}
for sentence, tags in training_data:
#     print(tags)
    for word in sentence:
        if word not in word_to_ix:
            word_to_ix[word] = len(word_to_ix)
print(word_to_ix)

 

 9.标签设置，将标签进行id化，导入模型，开始BiLSTM+CRF模型的训练

tag_to_ix = {"B": 0, "I": 1, "O": 2, START_TAG: 3, STOP_TAG: 4}

model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)

# Check predictions before training
# 训练前检查预测
with torch.no_grad():
    precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)
    precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long)
    print('样本一的真实标签：'+ str(precheck_tags.tolist()))
    print('未训练模型的预测：'+ str(model(precheck_sent)))

print('=============开始BiLSTM+CRF模型的训练=============')
# Make sure prepare_sequence from earlier in the LSTM section is loaded
for epoch in range(200):  # again, normally you would NOT do 300 epochs, it is toy data
    for sentence, tags in training_data:
        # Step 1. Remember that Pytorch accumulates gradients.
        # We need to clear them out before each instance
        model.zero_grad()

        # Step 2. Get our inputs ready for the network, that is,
        # turn them into Tensors of word indices.
        sentence_in = prepare_sequence(sentence, word_to_ix)
        targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)

        # Step 3. Run our forward pass.
        loss = model.neg_log_likelihood(sentence_in, targets)

        # Step 4. Compute the loss, gradients, and update the parameters by
        # calling optimizer.step()
        loss.backward()
        optimizer.step()
    if epoch%50 == 0:
        print(f'模型训练第{epoch}轮的Loss值为：{loss[0]}')

10.保存训练好的模型 

# 保存训练好的模型
output_path = 'ner_trained_model.h5'
torch.save(model, output_path)
print('=============训练结束，保存训练好的模型=============\n\n')

生成如下文件 

 

11.加载训练好的模型，进行文本标注，举了一个例子：

test_data = ['高', '血', '压', '容', '易', '造', '成', '心', '脏', '病']

# 加载训练好的模型
print('=============加载训练好的模型，进行测试=============')
test_data = ['高', '血', '压', '容', '易', '造', '成', '心', '脏', '病']
model_path = 'ner_trained_model.h5'
trained_ner_model = torch.load(model_path)
with torch.no_grad():
    precheck_sent = prepare_sequence(test_data, word_to_ix)
    result = model(precheck_sent)
    print('训练后模型的预测：' ,result)