Python vocab.Vocab方法代碼示例

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def build_vocabulary(datadir, outdir, glove_path):
    """Construct the vocabulary object used throughout."""
    # We're not going to backprop through the word vectors
    # both train and dev words end up in the vocab.
    counter = Counter()
    for split in splits:
        if split == "augmented":
            continue

        datapath = os.path.join(datadir, split + ".json")

        for question, context, _, _ in data_stream(datapath):
            for word in ciseau.tokenize(question, normalize_ascii=False):
                counter[normalize(word)] += 1
            for word in ciseau.tokenize(context, normalize_ascii=False):
                counter[normalize(word)] += 1

    common_words = [UNK, SOS, EOS, PAD] + [w for w, _ in counter.most_common()]

    vocab_path = os.path.join(outdir, "vocab.txt")
    with open(vocab_path, "wt") as handle:
        handle.write("\n".join(common_words))

    return Vocab(outdir) 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def main():
    if not os.path.exists(opt.model_save_file):
        os.makedirs(opt.model_save_file)
    vocab = Vocab(opt.emb_filename)
    log.info(f'Loading {opt.dataset} Datasets...')
    log.info(f'Domains: {opt.domains}')

    train_sets, dev_sets, test_sets, unlabeled_sets = {}, {}, {}, {}
    for domain in opt.domains:
        train_sets[domain], dev_sets[domain], test_sets[domain], unlabeled_sets[domain] = \
                get_fdu_mtl_datasets(vocab, opt.fdu_mtl_dir, domain, opt.max_seq_len)
    opt.num_labels = FduMtlDataset.num_labels
    log.info(f'Done Loading {opt.dataset} Datasets.')

    cv = train(vocab, train_sets, dev_sets, test_sets, unlabeled_sets)
    log.info(f'Training done...')
    acc = sum(cv['valid'].values()) / len(cv['valid'])
    log.info(f'Validation Set Domain Average\t{acc}')
    test_acc = sum(cv['test'].values()) / len(cv['test'])
    log.info(f'Test Set Domain Average\t{test_acc}')
    return cv 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def create(name, question_layers, document_layers, pick_end_word_layers,
           layer_size, beam_size, embedding_dropout, hidden_dropout,
           learning_rate, anneal_every, anneal_rate, clip_norm,
           l2_scale, weight_noise, replace, vocab_path, seed):
    """Create a new QA model."""
    vocab = Vocab(vocab_path)

    random.seed(seed)

    config = gnr.ModelConfig(
        vocab_size=vocab.size,
        question_layers=question_layers,
        document_layers=document_layers,
        pick_end_word_layers=pick_end_word_layers,
        layer_size=layer_size,
        beam_size=beam_size,
        embedding_dropout_prob=embedding_dropout,
        hidden_dropout_prob=hidden_dropout,
        learning_rate=learning_rate,
        anneal_every=anneal_every,
        anneal_rate=anneal_rate,
        clip_norm=clip_norm,
        l2_scale=l2_scale,
        weight_noise=weight_noise)

    create_model("gnr", name, config, gnr, vocab.word_embeddings, replace) 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def predict(name, data, vocab_path, output, batch_size):
    """Generate predictions for a trained model."""
    with session_with_model("gnr", name) as (session, qa_model):
        vocab = Vocab(vocab_path)

        eval_batches = []
        current_batch = []
        for question, context, _, qa_id in featurize.data_stream(data):
            current_batch.append((question, context, qa_id))

            if len(current_batch) == batch_size:
                eval_batches.append(current_batch)
                current_batch = []

        if len(current_batch) > 0:
            eval_batches.append(current_batch)

        predictions = {}
        for idx, batch in enumerate(eval_batches):
            questions, contexts, ids = list(zip(*batch))
            preds = evaluate_batch(
                session, qa_model, zip(questions, contexts), vocab)

            for i, pred in enumerate(preds):
                predictions[ids[i]] = pred

        with open(output, "wt") as handle:
            json.dump(predictions, handle)

        # run the evaluation code
        subprocess.check_call(["python", "evaluate.py", data, output]) 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def prepare(args):
    """
    checks data, creates the directories, prepare the vocabulary and embeddings
    """
    logger = logging.getLogger("brc")
    logger.info('Checking the data files...')
    for data_path in args.train_files + args.dev_files + args.test_files:
        assert os.path.exists(data_path), '{} file does not exist.'.format(data_path)
    logger.info('Preparing the directories...')
    for dir_path in [args.vocab_dir, args.model_dir, args.result_dir, args.summary_dir]:
        if not os.path.exists(dir_path):
            os.makedirs(dir_path)

    logger.info('Building vocabulary...')
    brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
                          args.train_files, args.dev_files, args.test_files)
    vocab = Vocab(lower=True)
    for word in brc_data.word_iter('train'):
        vocab.add(word)

    unfiltered_vocab_size = vocab.size()
    vocab.filter_tokens_by_cnt(min_cnt=2)
    filtered_num = unfiltered_vocab_size - vocab.size()
    logger.info('After filter {} tokens, the final vocab size is {}'.format(filtered_num,
                                                                            vocab.size()))

    logger.info('Assigning embeddings...')
    vocab.randomly_init_embeddings(args.embed_size)

    logger.info('Saving vocab...')
    with open(os.path.join(args.vocab_dir, 'vocab.data'), 'wb') as fout:
        pickle.dump(vocab, fout)

    logger.info('Done with preparing!') 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def prepare(logger, args):
    """
    checks data, creates the directories, prepare the vocabulary and embeddings
    """
    logger.info('Checking the data files...')
    for data_path in args.trainset + args.devset + args.testset:
        assert os.path.exists(data_path), '{} file does not exist.'.format(
            data_path)
    logger.info('Preparing the directories...')
    for dir_path in [args.vocab_dir, args.save_dir, args.result_dir]:
        if not os.path.exists(dir_path):
            os.makedirs(dir_path)

    logger.info('Building vocabulary...')
    brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
                          args.trainset, args.devset, args.testset)
    vocab = Vocab(lower=True)
    for word in brc_data.word_iter('train'):
        vocab.add(word)

    unfiltered_vocab_size = vocab.size()
    vocab.filter_tokens_by_cnt(min_cnt=2)
    filtered_num = unfiltered_vocab_size - vocab.size()
    logger.info('After filter {} tokens, the final vocab size is {}'.format(
        filtered_num, vocab.size()))

    logger.info('Assigning embeddings...')
    vocab.randomly_init_embeddings(args.embed_size)

    logger.info('Saving vocab...')
    with open(os.path.join(args.vocab_dir, 'vocab.data'), 'wb') as fout:
        pickle.dump(vocab, fout)

    logger.info('Done with preparing!') 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def load_data(small=True, char_based=False, batch_size=20, vocab_size=10000, history_len=5, max_tokens=50, null_mark=False):
    vocab_path = os.path.join(resource_dir, "ptb.train.txt")
    valid_path = os.path.join(resource_dir, "ptb.valid.txt")
    if small:
        train_path = os.path.join(resource_dir, "ptb.train.10k.txt")
    else:
        train_path = os.path.join(resource_dir, "ptb.train.txt")
    vocab = Vocab(char_based=char_based, null_mark=null_mark)
    vocab.load(vocab_path, max_size=vocab_size)

    lmdata = LMDataset(vocab, train_path, valid_path, history_len=-1, char_based=char_based, max_tokens=max_tokens)
    batch = BunchSequences(lmdata, batch_size=batch_size, fragment_length=history_len)
    return vocab, batch 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def load_word_vectors(path):
    if os.path.isfile(path + '.pth') and os.path.isfile(path + '.vocab'):
        print('==> File found, loading to memory')
        vectors = torch.load(path + '.pth')
        vocab = Vocab(filename=path + '.vocab')
        return vocab, vectors
    # saved file not found, read from txt file
    # and create tensors for word vectors
    print('==> File not found, preparing, be patient')
    count = sum(1 for line in open(path + '.txt'))
    with open(path + '.txt', 'r') as f:
        contents = f.readline().rstrip('\n').split(' ')
        dim = len(contents[1:])
    words = [None] * (count)
    vectors = torch.zeros(count, dim)
    with open(path + '.txt', 'r') as f:
        idx = 0
        for line in f:
            contents = line.rstrip('\n').split(' ')
            words[idx] = contents[0]
            vectors[idx] = torch.Tensor(list(map(float, contents[1:])))
            idx += 1
    with open(path + '.vocab', 'w') as f:
        for word in words:
            f.write(word + '\n')
    vocab = Vocab(filename=path + '.vocab')
    torch.save(vectors, path + '.pth')
    return vocab, vectors


# write unique words from a set of files to a new file 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def load_word_vectors(path):
    if os.path.isfile(path+'.pth') and os.path.isfile(path+'.vocab'):
        print('==> File found, loading to memory')
        vectors = torch.load(path+'.pth')
        vocab = Vocab(filename=path+'.vocab')
        return vocab, vectors
    # saved file not found, read from txt file
    # and create tensors for word vectors
    print('==> File not found, preparing, be patient')
    count = sum(1 for line in open(path+'.txt'))
    with open(path+'.txt','r') as f:
        contents = f.readline().rstrip('\n').split(' ')
        dim = len(contents[1:])
    words = [None]*(count)
    vectors = torch.zeros(count,dim)
    with open(path+'.txt','r') as f:
        idx = 0
        for line in f:
            contents = line.rstrip('\n').split(' ')
            words[idx] = contents[0]
            #vectors[idx] = torch.Tensor(map(float, contents[1:]))
            vectors[idx] = torch.Tensor(list(map(float, contents[1:])))
            idx += 1
    with open(path+'.vocab','w') as f:
        for word in words:
            f.write(word+'\n')
    vocab = Vocab(filename=path+'.vocab')
    torch.save(vectors, path+'.pth')
    return vocab, vectors

# write unique words from a set of files to a new file 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def main():
    if not os.path.exists(opt.model_save_file):
        os.makedirs(opt.model_save_file)
    log.info('Running the S-MAN + P-MoE + C-MoE model...')
    vocabs = {}
    tag_vocab = TagVocab()
    assert opt.use_wordemb or opt.use_charemb, "At least one of word or char embeddings must be used!"
    char_vocab = Vocab(opt.charemb_size)
    log.info(f'Loading Datasets...')
    log.info(f'Languages {opt.langs}')

    log.info('Loading Embeddings...')
    train_sets, dev_sets, test_sets, unlabeled_sets = {}, {}, {}, {}
    for lang in opt.all_langs:
        log.info(f'Building Vocab for {lang}...')
        vocabs[lang] = Vocab(opt.emb_size, opt.emb_filenames[lang])
        assert not opt.train_on_translation or not opt.test_on_translation
        if opt.dataset.lower() == 'conll':
            get_dataset_fn = get_conll_ner_datasets
            if opt.train_on_translation:
                get_dataset_fn = get_train_on_translation_conll_ner_datasets
            if opt.test_on_translation:
                get_dataset_fn = get_test_on_translation_conll_ner_datasets
            train_sets[lang], dev_sets[lang], test_sets[lang], unlabeled_sets[lang] = \
                    get_dataset_fn(vocabs[lang], char_vocab, tag_vocab, opt.conll_dir, lang)
        else:
            raise Exception(f"Unknown dataset {opt.dataset}")

    opt.num_labels = len(tag_vocab)
    log.info(f'Tagset: {tag_vocab.id2tag}')
    log.info(f'Done Loading Datasets.')

    cv = train(vocabs, char_vocab, tag_vocab, train_sets, dev_sets, test_sets, unlabeled_sets)
    log.info(f'Training done...')
    acc = sum(cv['valid'].values()) / len(cv['valid'])
    log.info(f'Validation Set Domain Average\t{acc}')
    test_acc = sum(cv['test'].values()) / len(cv['test'])
    log.info(f'Test Set Domain Average\t{test_acc}')
    return cv 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def main():
    if not os.path.exists(opt.model_save_file):
        os.makedirs(opt.model_save_file)
    log.info('Running the S-MAN + P-MoE + C-MoE model...')
    vocabs = {}
    assert opt.use_wordemb or opt.use_charemb, "At least one of word or char embeddings must be used!"
    char_vocab = Vocab(opt.charemb_size) if opt.use_charemb else None
    log.info(f'Loading Datasets...')
    log.info(f'Domain: {opt.domain}')
    log.info(f'Languages {opt.langs}')

    log.info('Loading Embeddings...')
    train_sets, dev_sets, test_sets, unlabeled_sets = {}, {}, {}, {}
    for lang in opt.all_langs:
        log.info(f'Building Vocab for {lang}...')
        vocabs[lang] = Vocab(opt.emb_size, opt.emb_filenames[lang])
        assert not opt.train_on_translation or not opt.test_on_translation
        train_sets[lang], dev_sets[lang], test_sets[lang], unlabeled_sets[lang] = \
                get_multi_lingual_amazon_datasets(vocabs[lang], char_vocab, opt.amazon_dir,
                                         opt.domain, lang, opt.max_seq_len)
    opt.num_labels = MultiLangAmazonDataset.num_labels
    log.info(f'Done Loading Datasets.')

    cv = train(vocabs, char_vocab, train_sets, dev_sets, test_sets, unlabeled_sets)
    log.info(f'Training done...')
    acc = sum(cv['valid'].values()) / len(cv['valid'])
    log.info(f'Validation Set Domain Average\t{acc}')
    test_acc = sum(cv['test'].values()) / len(cv['test'])
    log.info(f'Test Set Domain Average\t{test_acc}')
    return cv 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def prepro(args):
    logger = logging.getLogger("QANet")
    logger.info("====== preprocessing ======")
    logger.info('Checking the data files...')
    for data_path in args.train_files + args.dev_files + args.test_files:
        assert os.path.exists(data_path), '{} file does not exist.'.format(data_path)

    logger.info('Preparing the directories...')
    for dir_path in [args.vocab_dir, args.model_dir, args.result_dir, args.summary_dir]:
        if not os.path.exists(dir_path):
            os.makedirs(dir_path)

    logger.info('Building vocabulary...')
    dataloader = DataLoader(args.max_p_num, args.max_p_len, args.max_q_len, args.max_ch_len, 
                          args.train_files, args.dev_files, args.test_files)

    vocab = Vocab(lower=True)
    for word in dataloader.word_iter('train'):
        vocab.add_word(word)
        [vocab.add_char(ch) for ch in word]

    unfiltered_vocab_size = vocab.word_size()
    vocab.filter_words_by_cnt(min_cnt=2)
    filtered_num = unfiltered_vocab_size - vocab.word_size()
    logger.info('After filter {} tokens, the final vocab size is {}, char size is{}'.format(filtered_num,
                                                                            vocab.word_size(), vocab.char_size()))

    unfiltered_vocab_char_size = vocab.char_size()
    vocab.filter_chars_by_cnt(min_cnt=2)
    filtered_char_num = unfiltered_vocab_char_size - vocab.char_size()
    logger.info('After filter {} chars, the final char vocab size is {}'.format(filtered_char_num,
                                                                            vocab.char_size()))

    logger.info('Assigning embeddings...')
    if args.pretrained_word_path is not None:
        vocab.load_pretrained_word_embeddings(args.pretrained_word_path)
    else:
        vocab.randomly_init_word_embeddings(args.word_embed_size)
    
    if args.pretrained_char_path is not None:
        vocab.load_pretrained_char_embeddings(args.pretrained_char_path)
    else:
        vocab.randomly_init_char_embeddings(args.char_embed_size)

    logger.info('Saving vocab...')
    with open(os.path.join(args.vocab_dir, 'vocab.data'), 'wb') as fout:
        pickle.dump(vocab, fout)

    logger.info('====== Done with preparing! ======') 

# 需要導入模塊: import vocab [as 別名]
# 或者: from vocab import Vocab [as 別名]
def prepare(args):
    logger = logging.getLogger('SegEDU')
    logger.info('Randomly sample 10% of the training data for validation...')
    raw_train_dir = os.path.join(args.rst_dir, 'TRAINING')
    raw_dev_dir = os.path.join(args.rst_dir, 'DEV')
    if not os.path.exists(raw_dev_dir):
        os.makedirs(raw_dev_dir)
    raw_train_doc_ids = [file.split('.')[0] for file in os.listdir(raw_train_dir) if file.endswith('.out')]
    random.shuffle(raw_train_doc_ids)
    dev_doc_ids = raw_train_doc_ids[: int(len(raw_train_doc_ids) * 0.1)]
    for doc_id in dev_doc_ids:
        p = subprocess.call('mv {}/{}* {}'.format(raw_train_dir, doc_id, raw_dev_dir), shell=True)

    preprocessed_train_dir = os.path.join(args.rst_dir, 'preprocessed/train/')
    preprocessed_dev_dir = os.path.join(args.rst_dir, 'preprocessed/dev/')
    preprocessed_test_dir = os.path.join(args.rst_dir, 'preprocessed/test/')
    logger.info('Preprocessing Train data...')
    preprocess_rst_data(os.path.join(args.rst_dir, 'TRAINING'), preprocessed_train_dir)
    logger.info('Preprocessing Dev data...')
    preprocess_rst_data(os.path.join(args.rst_dir, 'DEV'), preprocessed_dev_dir)
    logger.info('Preprocessing Test data...')
    preprocess_rst_data(os.path.join(args.rst_dir, 'TEST'), preprocessed_test_dir)

    # logger.info('Building Vocab...')
    # train_files = [os.path.join(preprocessed_train_dir, filename)
    #                for filename in sorted(os.listdir(preprocessed_train_dir)) if filename.endswith('.preprocessed')]
    # dev_files = [os.path.join(preprocessed_dev_dir, filename)
    #              for filename in sorted(os.listdir(preprocessed_dev_dir)) if filename.endswith('.preprocessed')]
    # test_files = [os.path.join(preprocessed_test_dir, filename)
    #               for filename in sorted(os.listdir(preprocessed_test_dir)) if filename.endswith('.preprocessed')]
    # rst_data = RSTData(train_files=train_files, dev_files=dev_files, test_files=test_files)
    # word_vocab = Vocab(lower=False)
    # for word in rst_data.gen_all_words():
    #     word_vocab.add(word)
    #
    # logger.info('Loading pretrained embeddings for words...')
    # if args.word_embed_path:
    #     word_vocab.load_pretrained_embeddings(args.word_embed_path)
    # else:
    #     word_vocab.embed_dim = args.word_embed_size
    #
    # logger.info('Saving vocab...')
    # if not os.path.exists(os.path.dirname(args.word_vocab_path)):
    #     os.makedirs(os.path.dirname(args.word_vocab_path))
    # with open(args.word_vocab_path, 'wb') as fout:
    #     pickle.dump(word_vocab, fout)