from gensim.models.word2vec import Word2Vec
from spacy.en import English
from regression import BaseBowRegressor
from language import tokenize_document
# better tokenizer
nlp = English()
NUM_PARTITIONS = 70
WINDOW_SIZE = 4
VECTOR_SIZE = 100
MODEL_FILE = "w2v_%d_parts_%d_vector_%d_window" % (NUM_PARTITIONS, VECTOR_SIZE,
WINDOW_SIZE)
reviews_texts, useful_votes, funny_votes, cool_votes, review_stars = BaseBowRegressor.get_reviews_data(
range(1, NUM_PARTITIONS))
sentences = [tokenize_document(txt) for txt in enumerate(reviews_texts)]
# build the word2vec model and save it
w2v = Word2Vec(sentences=sentences,
size=VECTOR_SIZE,
alpha=0.025,
window=WINDOW_SIZE,
min_count=2,
sample=1e-5,
workers=4,
negative=10)
w2v.init_sims(replace=True)
w2v.save(MODEL_FILE)
reviews_train, _, funny_votes_train, _, _ = BaseBowRegressor.get_reviews_data(
PARTITIONS_TRAINING)
count = 0
for votes in funny_votes_train:
if votes > 0:
count += 1
print "Total non-zero votes: %d of %d" % (count, len(funny_votes_train))
print "Tokenizing"
NUM_ELEMENTS_TRAIN = None
NUM_ELEMENTS_TEST = None
reviews_tokens_train = [
language.tokenize_document(txt)
for txt in enumerate(reviews_train[:NUM_ELEMENTS_TRAIN])
]
X_train = tokens_to_word_vectors(reviews_tokens_train, model)
reviews_tokens_train = None
reviews_train = None
gc.collect()
X_train = np.array(X_train)
y_train = np.array(funny_votes_train[:NUM_ELEMENTS_TRAIN]).astype('float32')
maxlen = 100 # cut texts after this number of words
batch_size = 32
model = Word2Vec.load(WORD2VEC_MODEL)
reviews_train, _, funny_votes_train, _, _ = BaseBowRegressor.get_reviews_data(PARTITIONS_TRAINING)
count = 0
for votes in funny_votes_train:
if votes > 0:
count += 1
print "Total non-zero votes: %d of %d" % (count, len(funny_votes_train))
print "Tokenizing"
NUM_ELEMENTS_TRAIN = None
NUM_ELEMENTS_TEST = None
reviews_tokens_train = [language.tokenize_document(txt) for txt in enumerate(reviews_train[:NUM_ELEMENTS_TRAIN])]
X_train = tokens_to_word_vectors(reviews_tokens_train, model)
reviews_tokens_train = None
reviews_train = None
gc.collect()
X_train = np.array(X_train)
y_train = np.array(funny_votes_train[:NUM_ELEMENTS_TRAIN]).astype('float32')
maxlen = 100 # cut texts after this number of words
batch_size = 32
print("Pad sequences (samples x time)")
WORD2VEC_MODEL = "w2v_70_parts_100_vector_4_window"
PARTITIONS_TRAINING = range(1, 30) #15
PARTITIONS_TESTING = range(50, 53) #22
w2vmodel = Word2Vec.load(WORD2VEC_MODEL)
reviews_train, _, funny_votes_train, _, _ = BaseBowRegressor.get_reviews_data(
PARTITIONS_TRAINING)
reviews_train, labels_train = give_balanced_classes(reviews_train,
funny_votes_train)
print "Tokenizing"
NUM_ELEMENTS_TRAIN = None
NUM_ELEMENTS_TEST = None
reviews_tokens_train = [
language.tokenize_document((i, unicode(txt)))
for (i, txt) in enumerate(reviews_train[:NUM_ELEMENTS_TRAIN])
]
X_train = tokens_to_word_vectors(reviews_tokens_train, w2vmodel)
reviews_tokens_train = None
reviews_train = None
gc.collect()
X_train = np.array(X_train)
labels_train = np.array(labels_train[:NUM_ELEMENTS_TRAIN])
# Load test material
print "LOADING TEST DATA"
""" Script to compute word vectors from the reviews """ from gensim.models.word2vec import Word2Vec from spacy.en import English from regression import BaseBowRegressor from language import tokenize_document # better tokenizer nlp = English() NUM_PARTITIONS = 70 WINDOW_SIZE = 4 VECTOR_SIZE = 100 MODEL_FILE = "w2v_%d_parts_%d_vector_%d_window" % (NUM_PARTITIONS, VECTOR_SIZE, WINDOW_SIZE) reviews_texts, useful_votes, funny_votes, cool_votes, review_stars = BaseBowRegressor.get_reviews_data(range(1, NUM_PARTITIONS)) sentences = [tokenize_document(txt) for txt in enumerate(reviews_texts)] # build the word2vec model and save it w2v = Word2Vec(sentences=sentences, size=VECTOR_SIZE, alpha=0.025, window=WINDOW_SIZE, min_count=2, sample=1e-5, workers=4, negative=10) w2v.init_sims(replace=True) w2v.save(MODEL_FILE)