[Datasets] Fix to_tf when the input feature_columns is a list. (ray…

…-project#31228)

This fixes a bug in dataset.to_tf method. This method should returns a dictionary when input feature_columns is a list, but in the current release 2.2 and master branch it is returning a set, which also fails due to unhashable type.

Signed-off-by: tmynn <[email protected]>

python/ray/data/dataset.py

@@ -3204,7 +3204,9 @@ def convert_batch_to_tensors(
             if isinstance(columns, str):
                 return convert_ndarray_to_tf_tensor(batch[columns], type_spec=type_spec)
             return {
-                convert_ndarray_to_tf_tensor(batch[column], type_spec=type_spec[column])
+                column: convert_ndarray_to_tf_tensor(
+                    batch[column], type_spec=type_spec[column]
+                )
                 for column in columns
             }
 

python/ray/data/tests/test_dataset_tf.py

@@ -46,6 +46,27 @@ def test_element_spec_type_with_multiple_columns(self):
             for value in feature_output_signature.values()
         )
 
+        df = pd.DataFrame(
+            {"feature1": [0, 1, 2], "feature2": [3, 4, 5], "label": [0, 1, 1]}
+        )
+        ds = ray.data.from_pandas(df)
+        dataset = ds.to_tf(
+            feature_columns=["feature1", "feature2"],
+            label_columns="label",
+            batch_size=3,
+        )
+        feature_output_signature, _ = dataset.element_spec
+        assert isinstance(feature_output_signature, dict)
+        assert feature_output_signature.keys() == {"feature1", "feature2"}
+        assert all(
+            isinstance(value, tf.TypeSpec)
+            for value in feature_output_signature.values()
+        )
+        features, labels = next(iter(dataset))
+        assert (labels.numpy() == df["label"].values).all()
+        assert (features["feature1"].numpy() == df["feature1"].values).all()
+        assert (features["feature2"].numpy() == df["feature2"].values).all()
+
     def test_element_spec_name(self):
         ds = ray.data.from_items([{"spam": 0, "ham": 0}])