def test_ermlp_predict(self):
"""Test ERMLP's predict function."""
ermlp = ERMLP(config=ERMLP_CONFIG)
predictions = ermlp.predict(triples=TEST_TRIPLES)
self.assertEqual(len(predictions), len(TEST_TRIPLES))
self.assertTrue(type(predictions.shape[0]), float)
def test_ermlp_predict(self):
"""Test ERMLP's predict function."""
ermlp = ERMLP(**ERMLP_CONFIG)
ermlp.num_entities = ERMLP_CONFIG[NUM_ENTITIES]
ermlp.num_relations = ERMLP_CONFIG[NUM_RELATIONS]
predictions = ermlp.predict(triples=TEST_TRIPLES)
self.assertEqual(len(predictions), len(TEST_TRIPLES))
self.assertTrue(type(predictions.shape[0]), float)
def test_instantiate_ermlp(self):
"""Test that ERMLP can be instantiated."""
ermlp = ERMLP(**ERMLP_CONFIG)
ermlp.num_entities = ERMLP_CONFIG[NUM_ENTITIES]
ermlp.num_relations = ERMLP_CONFIG[NUM_RELATIONS]
self.assertIsNotNone(ermlp)
self.assertEqual(ermlp.num_entities, 5)
self.assertEqual(ermlp.num_relations, 5)
self.assertEqual(ermlp.embedding_dim, 2)
self.assertEqual(ermlp.margin_loss, 4)
def test_compute_scores_ermlp(self):
"""Test that SE's score function computes the scores correct."""
ermlp = ERMLP(config=ERMLP_CONFIG)
h_embs = torch.tensor([[1., 1.], [1., 1.]], dtype=torch.float)
r_embs = torch.tensor([[1., 1.], [2., 2.]], dtype=torch.float)
t_embs = torch.tensor([[2., 2.], [4., 4.]], dtype=torch.float)
scores = ermlp._compute_scores(h_embs, r_embs,
t_embs).detach().cpu().numpy().tolist()
self.assertEqual(len(scores), 2)
def test_instantiate_ermlp(self):
"""Test that ERMLP can be instantiated."""
ermlp = ERMLP(config=ERMLP_CONFIG)
self.assertIsNotNone(ermlp)
self.assertEqual(ermlp.num_entities, 5)
self.assertEqual(ermlp.num_relations, 5)
self.assertEqual(ermlp.embedding_dim, 2)
self.assertEqual(ermlp.margin_loss, 4)