build_vocab.py

"""Build vocabularies of words and labels from datasets"""

import argparse
from collections import Counter
import json
import os


parser = argparse.ArgumentParser()
parser.add_argument('--min_count_word', default=1, help="Minimum count for words in the dataset", type=int)
parser.add_argument('--min_count_tag', default=1, help="Minimum count for labels in the dataset", type=int)
parser.add_argument('--data_dir', default='data/SemEval2010_task8', help="Directory containing the dataset")


def save_to_txt(vocab, txt_path):
    """Writes one token per line, 0-based line id corresponds to the id of the token.

    Args:
        vocab: (iterable object) yields token
        txt_path: (stirng) path to vocab file
    """
    with open(txt_path, 'w') as f:
        for token in vocab:
            f.write(token + '\n')
            
def save_dict_to_json(d, json_path):
    """Saves dict to json file

    Args:
        d: (dict)
        json_path: (string) path to json file
    """
    with open(json_path, 'w') as f:
        d = {k: v for k, v in d.items()}
        json.dump(d, f, indent=4)

def update_vocab(txt_path, vocab):
    """Update word and label vocabulary from dataset"""
    with open(txt_path) as f:
        for i, line in enumerate(f):
            line = line.strip()
            if line.endswith('...'):
                line = line.rstrip('...')
            word_seq = line.split('\t')[-1].split(' ')
            vocab.update(word_seq)
    return i + 1

def update_labels(txt_path, labels):
    """Update label vocabulary from dataset"""
    with open(txt_path) as f:
        for i, line in enumerate(f):
            line = line.strip()  # one label per line
            labels.update([line])
    return i + 1


if __name__ == '__main__':
    args = parser.parse_args()

    # Build word vocab with train and test datasets
    print("Building word vocabulary...")
    words = Counter()
    size_train_sentences = update_vocab(os.path.join(args.data_dir, 'train/sentences.txt'), words)
    size_test_sentences = update_vocab(os.path.join(args.data_dir, 'test/sentences.txt'), words)
    print("- done.")

    # Build label vocab with train and test datasets
    print("Building label vocabulary...")
    labels = Counter()
    size_train_tags = update_labels(os.path.join(args.data_dir, 'train/labels.txt'), labels)
    size_test_tags = update_labels(os.path.join(args.data_dir, 'test/labels.txt'), labels)
    print("- done.")

    # Assert same number of examples in datasets
    assert size_train_sentences == size_train_tags
    assert size_test_sentences == size_test_tags

    # Only keep most frequent tokens
    words = sorted([tok for tok, count in words.items() if count >= args.min_count_word])
    labels = sorted([tok for tok, count in labels.items() if count >= args.min_count_tag])

    # Save vocabularies to text file
    print("Saving vocabularies to file...")
    save_to_txt(words, os.path.join(args.data_dir, 'words.txt'))
    save_to_txt(labels, os.path.join(args.data_dir, 'labels.txt'))
    print("- done.")

    # Save datasets properties in json file
    sizes = {
        'train_size': size_train_sentences,
        'test_size': size_test_sentences,
        'vocab_size': len(words),
        'num_tags': len(labels)
    }
    save_dict_to_json(sizes, os.path.join(args.data_dir, 'dataset_params.json'))

    # Logging sizes
    to_print = "\n".join("-- {}: {}".format(k, v) for k, v in sizes.items())
    print("Characteristics of the dataset:\n{}".format(to_print))