def test_word2vec_n_closest():
embedded = malaya.malaya_word2vec(256)
word_vector = malaya.Word2Vec(embedded['nce_weights'],
embedded['dictionary'])
word = 'anwar'
assert len(word_vector.n_closest(word=word, num_closest=8,
metric='cosine')) > 0
def test_word2vec_n_closest_without_similarity():
embedded = malaya.malaya_word2vec(256)
word_vector = malaya.Word2Vec(embedded['nce_weights'],
embedded['dictionary'])
word = 'anwar'
assert len(
word_vector.n_closest(
word=word, num_closest=8, metric='cosine',
return_similarity=False)) > 0
def test_word2vec_tsne():
embedded = malaya.malaya_word2vec(32)
word_vector = malaya.Word2Vec(embedded['nce_weights'],
embedded['dictionary'])
embed_2d, word_list = word_vector.project_2d(0, 100)
assert embed_2d.shape[1] == 2
def test_word2vec_analogy():
embedded = malaya.malaya_word2vec(256)
word_vector = malaya.Word2Vec(embedded['nce_weights'],
embedded['dictionary'])
assert len(word_vector.analogy('anwar', 'penjara', 'kerajaan', 5)) == 5
def word_count(str):
counts = dict()
words = str.split()
for word in words:
if word in counts:
counts[word] += 1
else:
counts[word] = 1
return counts
''' TEST DIFFERENT EMBEDDING PERFORMANCE ON DIFFERENT DATASET '''
embedded = malaya.malaya_word2vec(256)
print(len(embedded['dictionary']), embedded['nce_weights'].shape)
word_vector = malaya.Word2Vec(embedded['nce_weights'], embedded['dictionary'])
''' FEATURE SELECTION '''
tvec = TfidfVectorizer(max_features=100000, ngram_range=(1, 3))
x_train_tfidf = tvec.fit_transform(train_x)
chi2score = chi2(x_train_tfidf, train_y)[0]
plt.figure(figsize=(15, 10))
wscores = zip(tvec.get_feature_names(), chi2score)
wchi2 = sorted(wscores, key=lambda x: x[1])
topchi2 = list(zip(*wchi2[-20:]))
x = range(len(topchi2[1]))
labels = topchi2[0]
def test_word2vec_calculator_n_closest_without_similarity():
embedded = malaya.malaya_word2vec(256)
word_vector = malaya.Word2Vec(embedded['nce_weights'], embedded['dictionary'])
assert len(word_vector.calculator('anwar + mahathir', num_closest=8, metric='cosine', return_similarity=False))
def test_word2vec_calculator_bracket():
embedded = malaya.malaya_word2vec(256)
word_vector = malaya.Word2Vec(embedded['nce_weights'], embedded['dictionary'])
assert len(word_vector.calculator('(anwar+hadi) * mahathir', num_closest=8, metric='cosine'))
