7a125ea5e0113ec9d10d010884cdc78419bffe6e,python/baseline/dy/tagger/train.py,TaggerTrainerDyNet,process_output,#TaggerTrainerDyNet#Any#Any#Any#Any#Any#Any#,31

Before Change

        total_labels = 0
        truth_n = truth
        // For fscore
        gold_count = 0
        guess_count = 0
        overlap_count = 0
        // For each sentence
        for b in range(len(guess)):

            sentence_length = sentence_lengths[b]
            gold = truth_n[b, :sentence_length]
            sentence = guess[b]
            sentence = sentence[:sentence_length]
            correct_labels += np.sum(np.equal(sentence, gold))
            total_labels += sentence_length
            gold_chunks = to_spans(gold, self.idx2label, self.span_type)
            gold_count += len(gold_chunks)
            guess_chunks = to_spans(sentence, self.idx2label, self.span_type)
            guess_count += len(guess_chunks)

            overlap_chunks = gold_chunks & guess_chunks
            overlap_count += len(overlap_chunks)

            // Should we write a file out?  If so, we have to have txts
            if handle is not None:
                id = ids[b]

After Change

        total_labels = 0
        truth_n = truth
        // For fscore
        gold_chunks = []
        pred_chunks = []
        // For each sentence
        for b in range(len(guess)):

            sentence_length = sentence_lengths[b]
            gold = truth_n[b, :sentence_length]
            sentence = guess[b]
            sentence = sentence[:sentence_length]
            correct_labels += np.sum(np.equal(sentence, gold))
            total_labels += sentence_length
            gold_chunks.append(set(to_spans(gold, self.idx2label, self.span_type)))
            pred_chunks.append(set(to_spans(sentence, self.idx2label, self.span_type)))
            // Should we write a file out?  If so, we have to have txts
            if handle is not None:
                id = ids[b]

Instances