Reject features with different columns in IterableDataset.cast

[LeSingh1]

IterableDataset.cast() assigns the given features directly:

features = _fix_for_backward_compatible_features(features)
info = self._info.copy()
info.features = features

There is no check that the columns match the dataset's, which Dataset.cast() does perform. The worst case is an extra column: it is accepted silently and then fabricated in the data, because the declared features are applied to the examples on the fly.

ds = Dataset.from_dict({"x": [1, 2], "y": ["a", "b"]}).to_iterable_dataset()

new_features = Features({"x": Value("int64"), "y": Value("string"), "z": Value("int64")})
list(ds.cast(new_features))
# [{'x': 1, 'y': 'a', 'z': None}, {'x': 2, 'y': 'b', 'z': None}]

No error at any point — the dataset just grows a column that was never in it. The map-style call raises:

ValueError: The columns in features (['x', 'y', 'z']) must be identical as the columns in the dataset: ['x', 'y']

Missing and renamed columns are accepted too, and fail later with a CastError: Couldn't cast from deep in the Arrow cast, which doesn't say the column set is wrong.

Fix

Apply the same check and the same message as Dataset.cast() when the features are known:

if info.features is not None and sorted(features) != sorted(info.features):
    raise ValueError(...)

sorted() is used on both sides, exactly like the map-style version, so reordering the columns keeps working. Datasets whose features aren't resolved yet (info.features is None) are untouched.

Test

test_iterable_dataset_cast_with_different_columns, parametrized over a missing column, an extra column and a renamed column. All three fail on main.