Merge branch 'develop' into release/1.3.0

ampligraph/latent_features/models/EmbeddingModel.py

@@ -174,6 +174,10 @@ def __init__(self,
         verbose : bool
             Verbose mode.
         """
+        if (loss == "bce") ^ (self.name == "ConvE"):
+            raise ValueError('Invalid Model - Loss combination. '
+                             'ConvE model can be used with BCE loss only and vice versa.')
+
         # Store for restoring later.
         self.all_params = \
             {

tests/ampligraph/evaluation/test_protocol.py

@@ -24,28 +24,28 @@
 # test for #186
 def test_evaluate_performance_too_many_entities_warning():
     X = load_yago3_10()
-    model = TransE(batches_count=1000, seed=0, epochs=1, k=5, eta=1, verbose=True)
+    model = TransE(batches_count=200, seed=0, epochs=1, k=5, eta=1, verbose=True)
     model.fit(X['train'])
 
     # no entity list declared
     with pytest.warns(UserWarning):
-        evaluate_performance(X['test'][::1], model, verbose=True, corrupt_side='o')
+        evaluate_performance(X['test'][::100], model, verbose=True, corrupt_side='o')
 
     # with larger than threshold entity list
     with pytest.warns(UserWarning):
         # TOO_MANY_ENT_TH threshold is set to 50,000 entities. Using explicit value to comply with linting
         # and thus avoiding exporting unused global variable.
         entities_subset = np.union1d(np.unique(X["train"][:, 0]), np.unique(X["train"][:, 2]))[:50000]
-        evaluate_performance(X['test'][::1], model, verbose=True, corrupt_side='o', entities_subset=entities_subset)
+        evaluate_performance(X['test'][::100], model, verbose=True, corrupt_side='o', entities_subset=entities_subset)
 
     # with small entity list (no exception expected)
-    evaluate_performance(X['test'][::1], model, verbose=True, corrupt_side='o', entities_subset=entities_subset[:10])
+    evaluate_performance(X['test'][::100], model, verbose=True, corrupt_side='o', entities_subset=entities_subset[:10])
 
     # with smaller dataset, no entity list declared (no exception expected)
     X_wn18rr = load_wn18rr()
-    model_wn18 = TransE(batches_count=1000, seed=0, epochs=1, k=5, eta=1, verbose=True)
+    model_wn18 = TransE(batches_count=200, seed=0, epochs=1, k=5, eta=1, verbose=True)
     model_wn18.fit(X_wn18rr['train'])
-    evaluate_performance(X_wn18rr['test'][::1], model_wn18, verbose=True, corrupt_side='o')
+    evaluate_performance(X_wn18rr['test'][::100], model_wn18, verbose=True, corrupt_side='o')
 
 
 def test_evaluate_performance_filter_without_xtest():
@@ -76,6 +76,7 @@ def test_evaluate_performance_ranking_against_specified_entities():
     ranks = ranks.reshape(-1)
     assert(np.sum(ranks>len(entities_subset))==0)
 
+
 def test_evaluate_performance_ranking_against_shuffled_all_entities():
     """ Compares mrr of test set by using default protocol against all entities vs 
         mrr of corruptions generated by corrupting using entities_subset = all entities shuffled

tests/ampligraph/latent_features/test_models.py

@@ -19,6 +19,22 @@
 from ampligraph.evaluation.protocol import to_idx
 
 
+def test_conve_bce_combo():
+    # no exception
+    model = ConvE(loss='bce')
+
+    # no exception
+    model = TransE(loss='nll')
+
+    # Invalid combination. Hence exception.
+    with pytest.raises(ValueError):
+        model = TransE(loss='bce')
+
+    # Invalid combination. Hence exception.
+    with pytest.raises(ValueError):
+        model = ConvE(loss='nll')
+
+
 def test_large_graph_mode():
     set_entity_threshold(10)
     X = load_wn18()