Castor/sm-model/utils.py

# file input output 
# LATER model input output as well

import os
import sys
import numpy as np
from gensim.models.keyedvectors import KeyedVectors

import torch

import cPickle



# logging setup
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(levelname)s - %(message)s')
ch.setFormatter(formatter)
logger.addHandler(ch)



def load_bin_vec(fname, words):
  """
  Loads 300x1 word vecs from Google (Mikolov) word2vec
  """
  print fname
  vocab = set(words)
  word_vecs = {}
  with open(fname, "rb") as f:
    header = f.readline()
    vocab_size, layer1_size = map(int, header.split())
    binary_len = numpy.dtype('float32').itemsize * layer1_size
    print 'vocab_size, layer1_size', vocab_size, layer1_size
    count = 0
    for i, line in enumerate(xrange(vocab_size)):
      if i % 100000 == 0:
        print '.',
      word = []
      while True:
        ch = f.read(1)
        if ch == ' ':
            word = ''.join(word)
            break
        if ch != '\n':
            word.append(ch)
      if word in vocab:
        count += 1
        word_vecs[word] = numpy.fromstring(f.read(binary_len), dtype='float32')
      else:
          f.read(binary_len)
    print "done"
    print "Words found in wor2vec embeddings", count
    return word_vecs


def logargs(func):
    def inner(*args, **kwargs):
        logger.info('%s : %s %s' % (func.__name__, args, kwargs))
        return func(*args, **kwargs)
    return inner


def cache_word_embeddings(word_embeddings_file, cache_file):
    if not word_embeddings_file.endswith('.gz'):
        logger.warning( 'WARNING: expecting a .gz file. Is the {} in the correct format?'.format(word_embeddings_file))

    vocab_size, vec_dim = 0, 0

    if not os.path.exists(cache_file):
        # cache does not exist
        if not os.path.exists(os.path.dirname(cache_file)):
            # make cache folder if needed
            os.mkdir(os.path.dirname(cache_file))
        
        logger.info( 'caching the word embeddings in np.memmap format'     )   
        wv = KeyedVectors.load_word2vec_format(word_embeddings_file, binary=True)
        # print len(wv.syn0), wv.syn0.shape
        # print len(wv.syn0norm) if wv.syn0norm else None
        fp = np.memmap(cache_file, dtype=np.double, mode='w+', shape=wv.syn0.shape)
        fp[:] = wv.syn0[:]
        with open(cache_file + '.vocab', 'w') as f:
            logger.info( 'writing out vocab for {}'.format(word_embeddings_file))
            for _, w in sorted( (voc.index, word) for word, voc in wv.vocab.items()):
                print >> f, w.encode('utf-8')
        with open(cache_file + '.dimensions', 'w') as f:
            logger.info( 'writing out dimensions for {}'.format(word_embeddings_file))
            print >> f, wv.syn0.shape[0], wv.syn0.shape[1]
        vocab_size, vec_dim  =  wv.syn0.shape
        del fp, wv
        print 'cached {} into {}'.format(word_embeddings_file, cache_file)

    return vocab_size, vec_dim


def load_embedding_dimensions(cache_file):
    vocab_size, vec_dim = 0, 0
    with open(cache_file + '.dimensions') as d:
        vocab_size, vec_dim = [int(e) for e in d.read().strip().split()]
    return vocab_size, vec_dim

    
def load_cached_embeddings(cache_file, vocab_list, oov_vec = []):
    logger.debug( 'loading cached embeddings ')    

    with open(cache_file + '.dimensions') as d:
        vocab_size, vec_dim = [int(e) for e in d.read().strip().split()]

    W = np.memmap(cache_file, dtype=np.double, shape=(vocab_size, vec_dim))

    with open(cache_file + '.vocab') as f:
        logger.debug( 'loading vocab')
        w2v_vocab_list = map(str.strip, f.readlines())

    vocab_dict = {w:k for k,w in enumerate(w2v_vocab_list)}
    
    # Read w2v for vocab appears in Q and A
    w2v_dict = {}
    for word in vocab_list:
        if word in w2v_dict:
            continue
        if word in vocab_dict:
            w2v_dict[word] = W[vocab_dict[word]]
        else:
            w2v_dict[word] =  np.random.uniform(-0.25, 0.25, vec_dim) if len(oov_vec) == 0 else oov_vec
            #w2v_dict[word] = W[vocab_dict["unk"]]
    return w2v_dict


def read_in_dataset(dataset_folder, set_folder):
    """
    read in the data to return (question, sentence, label)
    set_folder = {train|dev|test}
    """
    max_q = 0
    max_s = 0
    set_path = os.path.join(dataset_folder, set_folder)
    len_q_list =[ len(line.strip().split()) for line in open(os.path.join(set_path, 'a.toks')).readlines() ]
    questions = [ line.strip() for line in open(os.path.join(set_path, 'a.toks')).readlines() ]
    len_s_list =[ len(line.strip().split()) for line in open(os.path.join(set_path, 'b.toks')).readlines() ]
    sentences = [ line.strip() for line in open(os.path.join(set_path, 'b.toks')).readlines() ]
    labels = np.array([ int(line.strip()) for line in open(os.path.join(set_path, 'sim.txt')).readlines() ])
    ext_feats = np.array([ map(float, line.strip().split(' ')) for line in open(os.path.join(set_path, 'overlap_feats.txt')).readlines() ])

    #y = torch.from_numpy(labels)
    #return questions, sentences, y

    vocab = [ line.strip() for line in open(os.path.join(dataset_folder, 'vocab.txt')).readlines() ]
    return questions, sentences, labels, vocab, max(len_q_list), max(len_s_list), ext_feats

def get_test_qids_labels(dataset_folder, set_folder):
    set_path = os.path.join(dataset_folder, set_folder)
    qids = [ line.strip() for line in open(os.path.join(set_path, 'id.txt')).readlines() ]
    labels = np.array([ int(line.strip()) for line in open(os.path.join(set_path, 'sim.txt')).readlines() ])
    return qids, labels


if __name__ == "__main__":
    
    vocab = [ "unk", "idontreallythinkthiswordexists", "hello" ]

    w2v_dict, vec_dim =  load_cached_embeddings("../../data/word2vec-models/aquaint+wiki.txt.gz.ndim=50.cache", vocab)
    
    for w, v in w2v_dict.iteritems():
        print w
        print v