def test_similar_by_sentence(self):
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
o = m.sv.similar_by_sentence(sentence=["the", "product", "is", "good"],
model=m)
self.assertEqual(4, o[0][0])
def test_similar_by_sentence_wrong_model(self):
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
with self.assertRaises(RuntimeError):
m.sv.similar_by_sentence(sentence=["the", "product", "is", "good"],
model=W2V)
def test_similar_by_word(self):
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
o = m.sv.similar_by_word(word="the", wv=m.wv)
self.assertEqual(96, o[0][0])
o = m.sv.similar_by_word(word="the", wv=m.wv, indexable=sentences)
self.assertEqual(96, o[0][1])
def test_most_similar_wrong_indexable(self):
def indexable(self):
pass
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
with self.assertRaises(RuntimeError):
m.sv.most_similar(positive=0, indexable=indexable)
def test_most_similar_vecs(self):
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
m.sv.init_sims()
v = m.sv[[0, 1]]
o = m.sv.most_similar(positive=v)
self.assertEqual(1, o[0][0])
self.assertEqual(0, o[1][0])
def test_most_similar_vec(self):
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
m.sv.init_sims()
v = m.sv.get_vector(0, use_norm=True)
o = m.sv.most_similar(positive=v)
# Includes 0 obviously
self.assertEqual(45, o[1][0])
self.assertEqual(35, o[2][0])
def test_most_similar(self):
sent_ind = IndexedList(SENTENCES)
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
o = m.sv.most_similar(positive=0)
self.assertEqual(45, o[0][0])
self.assertEqual(35, o[1][0])
o = m.sv.most_similar(positive=0, indexable=sentences)
self.assertEqual("Looks good and fits snug", o[0][0])
o = m.sv.most_similar(positive=0, indexable=sent_ind)
self.assertEqual("Looks good and fits snug".split(), o[0][0][0])
def test_most_similar_restrict_size_tuple(self):
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
o = m.sv.most_similar(positive=20, topn=20, restrict_size=(5, 25))
self.assertEqual(19, len(o))
self.assertEqual(22, o[0][0])
o = m.sv.most_similar(positive=1, topn=20, restrict_size=(5, 25))
self.assertEqual(20, len(o))
self.assertEqual(9, o[0][0])
o = m.sv.most_similar(positive=1, topn=20, restrict_size=(5, 25), indexable=sentences)
self.assertEqual(20, len(o))
self.assertEqual(9, o[0][1])
def test_similar_by_vector(self):
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
o = m.sv.similar_by_vector(m.wv["the"])
self.assertEqual(96, o[0][0])
def test_most_similar_restrict_size(self):
sentences = IndexedLineDocument(CORPUS)
m = Average(W2V)
m.train(sentences)
o = m.sv.most_similar(positive=20, topn=20, restrict_size=5)
self.assertEqual(5, len(o))
def setUp(self):
self.p = "fse/test/test_data/test_sentences.txt"
self.doc = IndexedLineDocument(self.p)
def setUp(self):
self.sentences = IndexedLineDocument(CORPUS)
self.model = SIF(W2V, lang_freq="en")
