def test_min_components(self):
try:
with self.assertRaises(ValueError):
TextWiser(Embedding.TfIdf(min_df=2),
Transformation.UMAP(n_components=1),
dtype=torch.float32)
except ModuleNotFoundError:
print('No UMAP found. Skipping the test. ...', end=" ", flush=True)
def test_fit_transform(self):
try:
tw = TextWiser(Embedding.TfIdf(min_df=1),
Transformation.UMAP(init='random',
n_neighbors=2,
n_components=2),
dtype=torch.float32)
expected = torch.tensor([[-12.1613626480, 22.0555286407],
[-11.3154125214, 22.4605998993],
[-10.7626724243, 21.6793708801]],
dtype=torch.float32)
self._test_fit_transform(tw, expected)
self._reset_seed()
self._test_fit_before_transform(tw, expected)
except ModuleNotFoundError:
print('No UMAP found. Skipping the test. ...', end=" ", flush=True)