import numpy as np import torch import torch.autograd as autograd import torch.nn as nn import torch.optim as optim from sklearn import metrics from datetime import datetime torch.manual_seed(1) def argmax(vec): # return the argmax as a python int _, idx = torch.max(vec, 1) return idx.item() def prepare_sequence(seq, to_ix): idxs = [to_ix[w] for w in seq] return torch.tensor(idxs, dtype=torch.long) # Compute log sum exp in a numerically stable way for the forward algorithm 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))) 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. 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. 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 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 START_TAG = "<START>" STOP_TAG = "<STOP>" EMBEDDING_DIM = 5 HIDDEN_DIM = 4 # Make up some training data with open('/home/dlindvai/work/text.txt', 'r') as text2: with open('/home/dlindvai/work/tags.txt', 'r') as tags2: text1 = text2.read().splitlines() tags1 = tags2.read().splitlines() for line in text1: text = line.replace("['", "").replace("']", "") for line in tags1: tags = line.replace("['", "").replace("']", "") training_data = [( text.split() , tags.split() )] #print(training_data) word_to_ix = {} for sentence, tags in training_data: for word in sentence: if word not in word_to_ix: word_to_ix[word] = len(word_to_ix) tag_to_ix = {"S": 0, "P": 1, "C": 2, "Q": 3, "N": 4, START_TAG: 5, STOP_TAG: 6} 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 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(model(precheck_sent)) # Print start time start = datetime.now() start_time = start.strftime("%H:%M:%S") print("Start time = ", start_time) for epoch in range(50): 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() # Check predictions after training with torch.no_grad(): precheck_sent = prepare_sequence(training_data[0][0], word_to_ix) #print(model(precheck_sent)) # Error calculator var = model(precheck_sent) y_true = np.array(targets) y_pred = np.array(var[1]) print(metrics.confusion_matrix(y_true, y_pred)) print(metrics.classification_report(y_true, y_pred, digits=3)) # Print finish time finish = datetime.now() finish_time = finish.strftime("%H:%M:%S") print("Finish time = ", finish_time)