print word2dfs.items()[:10]
#########
alphabet = Alphabet(start_feature_id=0)
alphabet.add('UNKNOWN_WORD_IDX')
add_to_vocab(answers, alphabet)
add_to_vocab(questions, alphabet)
basename = os.path.basename(train)
cPickle.dump(alphabet, open(os.path.join(outdir, 'vocab.pickle'), 'w'))
print "alphabet", len(alphabet)
embeddings_location = 'embeddings/aquaint+wiki.txt.gz.ndim=50.bin'
word2vec = load_bin_vec(embeddings_location, alphabet.keys())
dummy_word_idx = alphabet.fid
q_max_sent_length = max(map(lambda x: len(x), questions))
a_max_sent_length = max(map(lambda x: len(x), answers))
print 'q_max_sent_length', q_max_sent_length
print 'a_max_sent_length', a_max_sent_length
# Convert dev and test sets
for fname in [train, dev, test]:
print fname
# qids, questions, answers, labels = load_data(fname, stoplist)
qids, questions, answers, labels = load_data(fname)
overlap_feats = compute_overlap_features(questions,
stopwords = []
alphabet = Alphabet(start_feature_id=0)
alphabet.add('UNKNOWN_WORD_IDX_0')
vocab_dict = {}
for crous in [df_train, df_test]:
add_to_vocab(crous['question'], alphabet)
add_to_vocab(crous['answer'], alphabet)
print alphabet.fid
temp_vec = 0
vocab_array = np.zeros((alphabet.fid, ndim), dtype='float32')
for index in alphabet.keys():
vec = know_dict.get(index, None)
if vec is None:
vec = rng.uniform(-0.25, 0.25, ndim)
vec = list(vec)
vec = np.array(vec, dtype='float32')
random_word_count[0] += 1
if alphabet[index] == 0:
vec = np.zeros(ndim)
temp_vec += vec
vocab_array[alphabet[index]] = vec
temp_vec /= len(vocab_array)
for index, _ in enumerate(vocab_array):
vocab_array[index] -= temp_vec
Pickle.dump(alphabet, open(vocab_index_file, 'wb'))