Added tests for two minor changes in the TabPreprocessor and the Trainer

pytorch_widedeep/preprocessing/tab_preprocessor.py

@@ -295,7 +295,9 @@ class TabPreprocessor(BasePreprocessor):
             and self.continuous_cols is not None
             and len(np.intersect1d(self.cat_embed_cols, self.continuous_cols)) > 0
         ):
-            overlapping_cols = list(np.intersect1d(cat_embed_cols, continuous_cols))
+            overlapping_cols = list(
+                np.intersect1d(self.cat_embed_cols, self.continuous_cols)
+            )
             raise ValueError(
                 "Currently passing columns as both categorical and continuum is not supported."
                 " Please, choose one or the other for the following columns: {}".format(

tests/test_data_utils/test_du_tabular.py

@@ -294,3 +294,19 @@ def test_embed_sz_rule_of_thumb(rule):
         tab_preprocessor.embed_dim[col] == embed_szs[col] for col in embed_szs.keys()
     ]
     assert all(out)
+
+
+###############################################################################
+# Test Valuerror for repeated cols
+###############################################################################
+
+
+def test_overlapping_cols_valueerror():
+
+    embed_cols = ["col1", "col2"]
+    cont_cols = ["col1", "col2"]
+
+    with pytest.raises(ValueError):
+        tab_preprocessor = TabPreprocessor(  # noqa: F841
+            cat_embed_cols=embed_cols, continuous_cols=cont_cols
+        )

tests/test_model_functioning/test_fit_methods.py

@@ -300,3 +300,23 @@ def test_custom_dataloader():
     )
     # simply checking that runs with DataLoaderImbalanced
     assert "train_loss" in trainer.history.keys()
+
+
+##############################################################################
+# Test raise warning for multiclass classification
+##############################################################################
+
+
+def test_multiclass_warning():
+    wide = Wide(np.unique(X_wide).shape[0], 1)
+    deeptabular = TabMlp(
+        column_idx=column_idx,
+        cat_embed_input=embed_input,
+        continuous_cols=colnames[-5:],
+        mlp_hidden_dims=[32, 16],
+        mlp_dropout=[0.5, 0.5],
+    )
+    model = WideDeep(wide=wide, deeptabular=deeptabular)
+
+    with pytest.raises(ValueError):
+        trainer = Trainer(model, loss="multiclass", verbose=0)  # noqa: F841