def create_fse_model(sentences):
sentences = [sent["sentence"] for sent in sentences]
print("SIF create indexes for embeddings")
ft = load_fasttext_model()
model = SIF(ft)
idx_sentences = IndexedList(sentences)
model.train(idx_sentences)
return model, idx_sentences
def test_broken_vocab(self):
w2v = Word2Vec(min_count=1, size=DIM)
w2v.build_vocab([l.split() for l in open(CORPUS, "r")])
for k in w2v.wv.vocab:
w2v.wv.vocab[k].count = np.nan
model = SIF(w2v)
with self.assertRaises(RuntimeError):
model.train(self.sentences)
def test_save_issue(self):
model = SIF(W2V)
model.train(self.sentences)
p = Path("fse/test/test_data/test_emb.model")
model.save(str(p))
model = SIF.load(str(p))
p.unlink()
self.assertEqual(2, len(model.svd_res))
model.sv.similar_by_sentence("test sentence".split(), model=model)
def test_parameter_sanity(self):
with self.assertRaises(ValueError):
m = SIF(W2V, alpha=-1)
m._check_parameter_sanity()
with self.assertRaises(ValueError):
m = SIF(W2V, components=-1)
m._check_parameter_sanity()
with self.assertRaises(ValueError):
m = SIF(W2V)
m.word_weights = np.ones_like(m.word_weights) + 2
m._check_parameter_sanity()
def setUp(self):
self.sentences = IndexedLineDocument(CORPUS)
self.model = SIF(W2V, lang_freq="en")
class TestSIFFunctions(unittest.TestCase):
def setUp(self):
self.sentences = IndexedLineDocument(CORPUS)
self.model = SIF(W2V, lang_freq="en")
def test_parameter_sanity(self):
with self.assertRaises(ValueError):
m = SIF(W2V, alpha=-1)
m._check_parameter_sanity()
with self.assertRaises(ValueError):
m = SIF(W2V, components=-1)
m._check_parameter_sanity()
with self.assertRaises(ValueError):
m = SIF(W2V)
m.word_weights = np.ones_like(m.word_weights) + 2
m._check_parameter_sanity()
def test_pre_train_calls(self):
self.model._pre_train_calls()
def test_post_train_calls(self):
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model._post_train_calls()
self.assertTrue(np.allclose(self.model.sv.vectors, 0, atol=1e-5))
def test_post_train_calls_no_removal(self):
self.model.components = 0
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model._post_train_calls()
self.assertTrue(np.allclose(self.model.sv.vectors, 1, atol=1e-5))
def test_post_inference_calls(self):
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model._post_train_calls()
output = np.ones((200, 10), dtype=np.float32)
self.model._post_inference_calls(output=output)
self.assertTrue(np.allclose(output, 0, atol=1e-5))
def test_post_inference_calls_no_svd(self):
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model.svd_res = None
with self.assertRaises(RuntimeError):
self.model._post_inference_calls(output=None)
def test_post_inference_calls_no_removal(self):
self.model.components = 0
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model._post_train_calls()
self.model._post_inference_calls(output=None)
self.assertTrue(np.allclose(self.model.sv.vectors, 1, atol=1e-5))
def test_dtype_sanity_word_weights(self):
self.model.word_weights = np.ones_like(self.model.word_weights,
dtype=int)
with self.assertRaises(TypeError):
self.model._check_dtype_santiy()
def test_dtype_sanity_svd_vals(self):
self.model.svd_res = (
np.ones_like(self.model.word_weights, dtype=int),
np.array(0, dtype=np.float32),
)
with self.assertRaises(TypeError):
self.model._check_dtype_santiy()
def test_dtype_sanity_svd_vecs(self):
self.model.svd_res = (
np.array(0, dtype=np.float32),
np.ones_like(self.model.word_weights, dtype=int),
)
with self.assertRaises(TypeError):
self.model._check_dtype_santiy()
def test_compute_sif_weights(self):
cs = 1095661426
w = "Good"
pw = 1.916650481770269e-08
alpha = self.model.alpha
sif = alpha / (alpha + pw)
idx = self.model.wv.vocab[w].index
self.model._compute_sif_weights()
self.assertTrue(np.allclose(self.model.word_weights[idx], sif))
def test_train(self):
output = self.model.train(self.sentences)
self.assertEqual((100, 1450), output)
self.assertTrue(np.isfinite(self.model.sv.vectors).all())
self.assertEqual(2, len(self.model.svd_res))
def test_save_issue(self):
model = SIF(W2V)
model.train(self.sentences)
p = Path("fse/test/test_data/test_emb.model")
model.save(str(p))
model = SIF.load(str(p))
p.unlink()
self.assertEqual(2, len(model.svd_res))
model.sv.similar_by_sentence("test sentence".split(), model=model)
def test_broken_vocab(self):
w2v = Word2Vec(min_count=1, size=DIM)
w2v.build_vocab([l.split() for l in open(CORPUS, "r")])
for k in w2v.wv.vocab:
w2v.wv.vocab[k].count = np.nan
model = SIF(w2v)
with self.assertRaises(RuntimeError):
model.train(self.sentences)
class TestSIFFunctions(unittest.TestCase):
def setUp(self):
self.sentences = IndexedLineDocument(CORPUS)
self.model = SIF(W2V, lang_freq="en")
def test_parameter_sanity(self):
with self.assertRaises(ValueError):
m = SIF(W2V, alpha=-1)
m._check_parameter_sanity()
with self.assertRaises(ValueError):
m = SIF(W2V, components=-1)
m._check_parameter_sanity()
with self.assertRaises(ValueError):
m = SIF(W2V)
m.word_weights = np.ones_like(m.word_weights) + 2
m._check_parameter_sanity()
def test_pre_train_calls(self):
self.model._pre_train_calls()
def test_post_train_calls(self):
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model._post_train_calls()
self.assertTrue(np.allclose(self.model.sv.vectors, 0, atol=1e-5))
def test_post_train_calls_no_removal(self):
self.model.components = 0
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model._post_train_calls()
self.assertTrue(np.allclose(self.model.sv.vectors, 1, atol=1e-5))
def test_post_inference_calls(self):
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model._post_train_calls()
output = np.ones((200, 10), dtype=np.float32)
self.model._post_inference_calls(output=output)
self.assertTrue(np.allclose(output, 0, atol=1e-5))
def test_post_inference_calls_no_svd(self):
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model.svd_res = None
with self.assertRaises(RuntimeError):
self.model._post_inference_calls(output=None)
def test_post_inference_calls_no_removal(self):
self.model.components = 0
self.model.sv.vectors = np.ones((200, 10), dtype=np.float32)
self.model._post_train_calls()
self.model._post_inference_calls(output=None)
self.assertTrue(np.allclose(self.model.sv.vectors, 1, atol=1e-5))
def test_dtype_sanity_word_weights(self):
self.model.word_weights = np.ones_like(self.model.word_weights,
dtype=int)
with self.assertRaises(TypeError):
self.model._check_dtype_santiy()
def test_dtype_sanity_svd_vals(self):
self.model.svd_res = (np.ones_like(self.model.word_weights, dtype=int),
np.array(0, dtype=np.float32))
with self.assertRaises(TypeError):
self.model._check_dtype_santiy()
def test_dtype_sanity_svd_vecs(self):
self.model.svd_res = (np.array(0, dtype=np.float32),
np.ones_like(self.model.word_weights, dtype=int))
with self.assertRaises(TypeError):
self.model._check_dtype_santiy()
def test_compute_sif_weights(self):
cs = 1095661426
w = "Good"
pw = 1.916650481770269e-08
alpha = self.model.alpha
sif = alpha / (alpha + pw)
idx = self.model.wv.vocab[w].index
self.model._compute_sif_weights()
self.assertTrue(np.allclose(self.model.word_weights[idx], sif))
def test_train(self):
output = self.model.train(self.sentences)
self.assertEqual((100, 1450), output)
self.assertTrue(np.isfinite(self.model.sv.vectors).all())