data_utils.py

"""Most utility functions here has been adopted from: 
https://github.com/guillaumegenthial/sequence_tagging/blob/master/model/data_utils.py
"""

import numpy as np
import os
import math

# shared global variables to be imported from model also
UNK = "$UNK$"
NUM = "$NUM$"
NONE = "O"

# read the word importance scores
class AnnotationDataset(object):
    def __init__(self, filename, processing_word=None):
        self.filename = filename
        self.processing_word = processing_word
        self.length = None

    def __iter__(self):
        with open(self.filename) as f:
            words, tags = [], []
            for line in f:
                line = line.strip()
                if (len(line) == 0):
                    if len(words) != 0:
                        yield words, tags
                        words, tags = [], []
                else:
                    ls = line.split(' ')
                    word, tag = ls[0], ls[-1]
                    if self.processing_word is not None:
                        word = self.processing_word(word)
                    words += [word]
                    tags += [tag]


    def __len__(self):
        if self.length is None:
            self.length = 0
            for _ in self:
                self.length += 1
        return self.length

def get_vocabs(datasets):
    print("Building vocab...")
    vocab_words = set()
    vocab_tags = set()
    for dataset in datasets:
        for words, tags in dataset:
            vocab_words.update(words)
            vocab_tags.update(tags)
    print("- done. {} tokens".format(len(vocab_words)))
    return vocab_words, vocab_tags


def get_char_vocab(dataset):
    vocab_char = set()
    for words, _ in dataset:
        for word in words:
            vocab_char.update(word)

    return vocab_char


def get_glove_vocab(filename):
    print("Building vocab...")
    vocab = set()
    with open(filename) as f:
        for line in f:
            word = line.strip().split(' ')[0]
            vocab.add(word)
    print("- done. {} tokens".format(len(vocab)))
    return vocab

def get_google_vocab(filename):
    from gensim.models import Word2Vec
    model = Word2Vec.load_word2vec_format(filename, binary=True)

    print ("Building vocab...")
    vocab = set(model.vocab.keys())
    
    print ("- done. {} tokens".format(len(vocab)))
    return model, vocab


def get_senna_vocab(filename):
    print ("Building vocab...")
    vocab = set()
    with open(filename) as f:
        for line in f:
            word = line.strip()
            vocab.add(word)
    print ("- done. {} tokens".format(len(vocab)))
    return vocab


def write_vocab(vocab, filename):
    print("Writing vocab...")
    with open(filename, "w") as f:
        for i, word in enumerate(vocab):
            if i != len(vocab) - 1:
                f.write("{}\n".format(word))
            else:
                f.write(word)
    print("- done. {} tokens".format(len(vocab)))


def load_vocab(filename):
    try:
        d = dict()
        with open(filename) as f:
            for idx, word in enumerate(f):
                word = word.strip()
                d[word] = idx

    except IOError:
        raise MyIOError(filename)
    return d


def export_trimmed_glove_vectors(vocab, glove_filename, trimmed_filename, dim):
    embeddings = np.zeros([len(vocab), dim])
    with open(glove_filename) as f:
        for line in f:
            line = line.strip().split(' ')
            word = line[0]
            embedding = [float(x) for x in line[1:]]
            if word in vocab:
                word_idx = vocab[word]
                embeddings[word_idx] = np.asarray(embedding)

    np.savez_compressed(trimmed_filename, embeddings=embeddings)


def export_trimmed_google_vectors(vocab, google_model, trimmed_filename, dim, random):
    embeddings = np.asarray(random.normal(loc=0.0, scale=0.1, size= [len(vocab), dim]), dtype=np.float32)
    for word in google_model.vocab.keys():
        if word in vocab:
            word_idx = vocab[word]
            embedding = google_model[word]
            embeddings[word_idx] = np.asarray(embedding)

    np.savez_compressed(trimmed_filename, embeddings=embeddings)


def export_trimmed_senna_vectors(vocab, vocab_emb, senna_filename, trimmed_filename, dim):
    embeddings = np.zeros([len(vocab), dim])
    vocab_emb = list(vocab_emb)
    with open(senna_filename) as f:
        for i, line in enumerate(f):
            line = line.strip().split(' ')
            word = vocab_emb[i]
            embedding = map(float, line)
            if word in vocab:
                word_idx = vocab[word]
                embeddings[word_idx] = np.asarray(embedding)

    np.savez_compressed(trimmed_filename, embeddings=embeddings)


def get_trimmed_glove_vectors(filename):
    try:
        with np.load(filename) as data:
            return data["embeddings"]

    except IOError:
        raise MyIOError(filename)


def get_trimmed_vectors(filename):
    return get_trimmed_glove_vectors(filename)


def get_processing_word(vocab_words=None, vocab_chars=None,
                    lowercase=False, chars=False):
    def f(word):
        # 0. get chars of words
        if vocab_chars is not None and chars == True:
            char_ids = []
            for char in word:
                # ignore chars out of vocabulary
                if char in vocab_chars:
                    char_ids += [vocab_chars[char]]

        # 1. preprocess word
        if lowercase:
            word = word.lower()
        if word.isdigit():
            word = NUM

        # 2. get id of word
        if vocab_words is not None:
            if word in vocab_words:
                word = vocab_words[word]
            else:
                word = vocab_words[UNK]

        # 3. return tuple char ids, word id
        if vocab_chars is not None and chars == True:
            return char_ids, word
        else:
            return word

    return f


def _pad_sequences(sequences, pad_tok, max_length):
    sequence_padded, sequence_length = [], []

    for seq in sequences:
        seq = list(seq)
        seq_ = seq[:max_length] + [pad_tok]*max(max_length - len(seq), 0)
        sequence_padded +=  [seq_]
        sequence_length += [min(len(seq), max_length)]

    return sequence_padded, sequence_length


def pad_sequences(sequences, pad_tok, nlevels=1):
    if nlevels == 1:
        max_length = max(map(lambda x : len(x), sequences))
        sequence_padded, sequence_length = _pad_sequences(sequences,
                                            pad_tok, max_length)

    elif nlevels == 2:
        max_length_word = max([max(map(lambda x: len(x), seq)) for seq in sequences])
        sequence_padded, sequence_length = [], []
        for seq in sequences:
            # all words are same length now
            sp, sl = _pad_sequences(seq, pad_tok, max_length_word)
            sequence_padded += [sp]
            sequence_length += [sl]

        max_length_sentence = max(map(lambda x : len(x), sequences))
        sequence_padded, _ = _pad_sequences(sequence_padded, [pad_tok]*max_length_word,
                                            max_length_sentence)
        sequence_length, _ = _pad_sequences(sequence_length, 0, max_length_sentence)


    return sequence_padded, sequence_length


def minibatches(data, minibatch_size):
    x_batch, y_batch = [], []
    for (x, y) in data:
        if len(x_batch) == minibatch_size:
            yield x_batch, y_batch
            x_batch, y_batch = [], []

        if type(x[0]) == tuple:
            x = zip(*x)
        x_batch += [x]
        y_batch += [y]

    if len(x_batch) != 0:
        yield x_batch, y_batch