Merge branch 'main' into get_feature_names

molpipeline/estimators/chemprop/models.py

@@ -143,8 +143,7 @@ def _predict(
         test_data = build_dataloader(X, num_workers=self.n_jobs, shuffle=False)
         predictions = self.lightning_trainer.predict(self.model, test_data)
         prediction_array = np.vstack(predictions)  # type: ignore
-        prediction_array = prediction_array.squeeze()
-
+        prediction_array = prediction_array.squeeze(axis=1)
         # Check if the predictions have the same length as the input dataset
         if prediction_array.shape[0] != len(X):
             raise AssertionError(

test_extras/test_chemprop/test_chemprop_pipeline.py

@@ -307,6 +307,12 @@ def test_prediction(self) -> None:
         pred_copy = model_copy.predict(molecule_net_logd_df["smiles"].tolist())
         self.assertTrue(np.allclose(pred, pred_copy))
 
+        # Test single prediction, this was causing an error before
+        single_mol_pred = regression_model.predict(
+            [molecule_net_logd_df["smiles"].iloc[0]]
+        )
+        self.assertEqual(single_mol_pred.shape, (1,))
+
 
 class TestClassificationPipeline(unittest.TestCase):
     """Test the Chemprop model pipeline for classification."""
@@ -341,6 +347,16 @@ def test_prediction(self) -> None:
         self.assertEqual(proba.shape, proba_copy.shape)
         self.assertTrue(np.allclose(proba[~nan_indices], proba_copy[~nan_indices]))
 
+        # Test single prediction, this was causing an error before
+        single_mol_pred = classification_model.predict(
+            [molecule_net_bbbp_df["smiles"].iloc[0]]
+        )
+        self.assertEqual(single_mol_pred.shape, (1,))
+        single_mol_proba = classification_model.predict_proba(
+            [molecule_net_bbbp_df["smiles"].iloc[0]]
+        )
+        self.assertEqual(single_mol_proba.shape, (1, 2))
+
 
 class TestMulticlassClassificationPipeline(unittest.TestCase):
     """Test the Chemprop model pipeline for multiclass classification."""
@@ -375,6 +391,16 @@ def test_prediction(self) -> None:
         self.assertEqual(pred.shape, pred_copy.shape)
         self.assertTrue(np.allclose(proba[~nan_mask], proba_copy[~nan_mask]))
 
+        # Test single prediction, this was causing an error before
+        single_mol_pred = classification_model.predict(
+            [test_data_df["Molecule"].iloc[0]]
+        )
+        self.assertEqual(single_mol_pred.shape, (1,))
+        single_mol_proba = classification_model.predict_proba(
+            [test_data_df["Molecule"].iloc[0]]
+        )
+        self.assertEqual(single_mol_proba.shape, (1, 3))
+
         with self.assertRaises(ValueError):
             classification_model.fit(
                 mols,