247 lines
9.2 KiB
Python
247 lines
9.2 KiB
Python
import numpy as np
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import torch
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import torch.autograd as autograd
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import torch.nn as nn
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import torch.optim as optim
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from sklearn import metrics
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from datetime import datetime
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torch.manual_seed(1)
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def argmax(vec):
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# return the argmax as a python int
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_, idx = torch.max(vec, 1)
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return idx.item()
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def prepare_sequence(seq, to_ix):
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idxs = [to_ix[w] for w in seq]
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return torch.tensor(idxs, dtype=torch.long)
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# Compute log sum exp in a numerically stable way for the forward algorithm
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def log_sum_exp(vec):
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max_score = vec[0, argmax(vec)]
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max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
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return max_score + \
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torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))
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class BiLSTM_CRF(nn.Module):
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def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
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super(BiLSTM_CRF, self).__init__()
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self.embedding_dim = embedding_dim
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self.hidden_dim = hidden_dim
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self.vocab_size = vocab_size
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self.tag_to_ix = tag_to_ix
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self.tagset_size = len(tag_to_ix)
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self.word_embeds = nn.Embedding(vocab_size, embedding_dim)
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self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,
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num_layers=1, bidirectional=True)
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# Maps the output of the LSTM into tag space.
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self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)
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# Matrix of transition parameters. Entry i,j is the score of
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# transitioning *to* i *from* j.
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self.transitions = nn.Parameter(
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torch.randn(self.tagset_size, self.tagset_size))
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# These two statements enforce the constraint that we never transfer
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# to the start tag and we never transfer from the stop tag
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self.transitions.data[tag_to_ix[START_TAG], :] = -10000
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self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000
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self.hidden = self.init_hidden()
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def init_hidden(self):
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return (torch.randn(2, 1, self.hidden_dim // 2),
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torch.randn(2, 1, self.hidden_dim // 2))
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def _forward_alg(self, feats):
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# Do the forward algorithm to compute the partition function
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init_alphas = torch.full((1, self.tagset_size), -10000.)
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# START_TAG has all of the score.
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init_alphas[0][self.tag_to_ix[START_TAG]] = 0.
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# Wrap in a variable so that we will get automatic backprop
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forward_var = init_alphas
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# Iterate through the sentence
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for feat in feats:
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alphas_t = [] # The forward tensors at this timestep
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for next_tag in range(self.tagset_size):
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# broadcast the emission score: it is the same regardless of
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# the previous tag
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emit_score = feat[next_tag].view(
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1, -1).expand(1, self.tagset_size)
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# the ith entry of trans_score is the score of transitioning to
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# next_tag from i
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trans_score = self.transitions[next_tag].view(1, -1)
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# The ith entry of next_tag_var is the value for the
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# edge (i -> next_tag) before we do log-sum-exp
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next_tag_var = forward_var + trans_score + emit_score
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# The forward variable for this tag is log-sum-exp of all the
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# scores.
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alphas_t.append(log_sum_exp(next_tag_var).view(1))
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forward_var = torch.cat(alphas_t).view(1, -1)
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terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
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alpha = log_sum_exp(terminal_var)
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return alpha
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def _get_lstm_features(self, sentence):
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self.hidden = self.init_hidden()
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embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)
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lstm_out, self.hidden = self.lstm(embeds, self.hidden)
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lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
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lstm_feats = self.hidden2tag(lstm_out)
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return lstm_feats
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def _score_sentence(self, feats, tags):
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# Gives the score of a provided tag sequence
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score = torch.zeros(1)
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tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])
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for i, feat in enumerate(feats):
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score = score + \
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self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]
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score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]
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return score
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def _viterbi_decode(self, feats):
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backpointers = []
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# Initialize the viterbi variables in log space
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init_vvars = torch.full((1, self.tagset_size), -10000.)
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init_vvars[0][self.tag_to_ix[START_TAG]] = 0
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# forward_var at step i holds the viterbi variables for step i-1
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forward_var = init_vvars
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for feat in feats:
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bptrs_t = [] # holds the backpointers for this step
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viterbivars_t = [] # holds the viterbi variables for this step
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for next_tag in range(self.tagset_size):
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# next_tag_var[i] holds the viterbi variable for tag i at the
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# previous step, plus the score of transitioning
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# from tag i to next_tag.
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# We don't include the emission scores here because the max
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# does not depend on them (we add them in below)
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next_tag_var = forward_var + self.transitions[next_tag]
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best_tag_id = argmax(next_tag_var)
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bptrs_t.append(best_tag_id)
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viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))
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# Now add in the emission scores, and assign forward_var to the set
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# of viterbi variables we just computed
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forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
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backpointers.append(bptrs_t)
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# Transition to STOP_TAG
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terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
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best_tag_id = argmax(terminal_var)
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path_score = terminal_var[0][best_tag_id]
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# Follow the back pointers to decode the best path.
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best_path = [best_tag_id]
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for bptrs_t in reversed(backpointers):
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best_tag_id = bptrs_t[best_tag_id]
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best_path.append(best_tag_id)
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# Pop off the start tag (we dont want to return that to the caller)
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start = best_path.pop()
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assert start == self.tag_to_ix[START_TAG] # Sanity check
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best_path.reverse()
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return path_score, best_path
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def neg_log_likelihood(self, sentence, tags):
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feats = self._get_lstm_features(sentence)
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forward_score = self._forward_alg(feats)
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gold_score = self._score_sentence(feats, tags)
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return forward_score - gold_score
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def forward(self, sentence): # dont confuse this with _forward_alg above.
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# Get the emission scores from the BiLSTM
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lstm_feats = self._get_lstm_features(sentence)
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# Find the best path, given the features.
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score, tag_seq = self._viterbi_decode(lstm_feats)
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return score, tag_seq
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START_TAG = "<START>"
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STOP_TAG = "<STOP>"
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EMBEDDING_DIM = 5
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HIDDEN_DIM = 4
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# Make up some training data
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with open('/home/dlindvai/work/text.txt', 'r') as text2:
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with open('/home/dlindvai/work/tags.txt', 'r') as tags2:
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text1 = text2.read().splitlines()
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tags1 = tags2.read().splitlines()
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for line in text1:
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text = line.replace("['", "").replace("']", "")
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for line in tags1:
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tags = line.replace("['", "").replace("']", "")
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training_data = [( text.split() , tags.split() )]
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#print(training_data)
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word_to_ix = {}
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for sentence, tags in training_data:
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for word in sentence:
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if word not in word_to_ix:
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word_to_ix[word] = len(word_to_ix)
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tag_to_ix = {"S": 0, "P": 1, "C": 2, "Q": 3, "N": 4, START_TAG: 5, STOP_TAG: 6}
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model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
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optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)
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# Check predictions before training
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with torch.no_grad():
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precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)
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precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long)
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#print(model(precheck_sent))
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# Print start time
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start = datetime.now()
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start_time = start.strftime("%H:%M:%S")
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print("Start time = ", start_time)
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for epoch in range(50):
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for sentence, tags in training_data:
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# Step 1. Remember that Pytorch accumulates gradients. We need to clear them out before each instance
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model.zero_grad()
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# Step 2. Get our inputs ready for the network, that is, turn them into Tensors of word indices.
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sentence_in = prepare_sequence(sentence, word_to_ix)
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targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)
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# Step 3. Run our forward pass.
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loss = model.neg_log_likelihood(sentence_in, targets)
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# Step 4. Compute the loss, gradients, and update the parameters by calling optimizer.step()
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loss.backward()
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optimizer.step()
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# Check predictions after training
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with torch.no_grad():
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precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)
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#print(model(precheck_sent))
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# Error calculator
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var = model(precheck_sent)
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y_true = np.array(targets)
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y_pred = np.array(var[1])
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print(metrics.confusion_matrix(y_true, y_pred))
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print(metrics.classification_report(y_true, y_pred, digits=3))
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# Print finish time
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finish = datetime.now()
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finish_time = finish.strftime("%H:%M:%S")
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print("Finish time = ", finish_time)
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