def test_simple(self):
X = ["The light crashes"]
m = MapToSynsets()
Z = m.transform(X)
self.assertEqual(len(Z), 1)
self.assertTrue(isinstance(Z[0], str))
for word in ["light.a.01", "crash.v.01"]:
self.assertIn(word, Z[0])
self.assertNotIn("crash.n.02", Z[0])
def test_simple(self):
X = ["The light crashes"]
m = MapToSynsets()
Z = m.transform(X)
self.assertEqual(len(Z), 1)
self.assertTrue(isinstance(Z[0], str))
for word in ["light.a.01", "crash.v.01"]:
self.assertIn(word, Z[0])
self.assertNotIn("crash.n.02", Z[0])
def build_synset_extraction(binary, min_df, ngram, useTfIdf):
if useTfIdf:
return make_pipeline(
MapToSynsets(),
TfidfVectorizer(min_df=min_df,
max_df=0.8,
sublinear_tf=True,
use_idf=True,
ngram_range=(1, 3)), ClassifierOvOAsFeatures())
return make_pipeline(
MapToSynsets(),
CountVectorizer(binary=binary,
tokenizer=lambda x: x.split(),
min_df=min_df,
ngram_range=(1, ngram)), ClassifierOvOAsFeatures())
def build_synset_extraction(binary, min_df, ngram):
return make_pipeline(MapToSynsets(),
CountVectorizer(binary=binary,
tokenizer=lambda x: x.split(),
min_df=min_df,
ngram_range=(1, ngram)),
ClassifierOvOAsFeatures())
def test_fit_returns_self(self):
m = MapToSynsets()
s = m.fit([])
self.assertEqual(s, m)
def test_empty(self):
m = MapToSynsets()
Z = m.transform([])
self.assertEqual(len(Z), 0)
def test_fit_returns_self(self):
m = MapToSynsets()
s = m.fit([])
self.assertEqual(s, m)
def test_empty(self):
m = MapToSynsets()
Z = m.transform([])
self.assertEqual(len(Z), 0)
