def test_set_params(self):
# Set the arguments in container classes
tw = TextWiser(Embedding.TfIdf(min_df=5),
Transformation.NMF(n_components=30),
lazy_load=True)
tw.set_params(embedding__min_df=10,
transformations__0__n_components=10)
self.assertEqual(tw.embedding.min_df, 10)
self.assertEqual(tw.transformations[0].n_components, 10)
# Set the arguments in implementation
tw = TextWiser(Embedding.Doc2Vec(vector_size=2, min_count=1,
workers=1))
tw.fit(docs)
tw.set_params(_imp__0__seed=10)
self.assertEqual(tw._imp[0].seed, 10)
# Set the arguments in a schema
schema = {'transform': ['tfidf', ['nmf', {'n_components': 30}]]}
tw = TextWiser(Embedding.Compound(schema=schema))
tw.set_params(embedding__schema__transform__0__min_df=10,
embedding__schema__transform__1__n_components=10)
self.assertEqual(tw.embedding.schema['transform'][0][1]['min_df'], 10)
self.assertEqual(
tw.embedding.schema['transform'][1][1]['n_components'], 10)
# Replace a part of the schema in a list
tw.set_params(embedding__schema__transform__0='bow')
self.assertEqual(tw.embedding.schema['transform'][0], 'bow')
# Replace a part of the schema
tw.set_params(embedding__schema__transform=['bow'])
self.assertEqual(tw.embedding.schema['transform'][0], 'bow')
def test_fit_transform(self):
tw = TextWiser(Embedding.TfIdf(min_df=2),
Transformation.NMF(n_components=2),
dtype=torch.float32)
expected = torch.tensor(
[[0.8865839243, 0.0000000000], [0.6736079454, 0.5221673250],
[0.0203559380, 1.1122620106]],
dtype=torch.float32)
self._test_fit_transform(tw, expected)
self._reset_seed()
self._test_fit_before_transform(tw, expected, atol=1e-5)
def test_min_components(self):
with self.assertRaises(ValueError):
TextWiser(Embedding.TfIdf(min_df=2),
Transformation.NMF(n_components=1),
dtype=torch.float32)