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)