def test_iterable_dataset_cast(generate_examples_fn):
ex_iterable = ExamplesIterable(generate_examples_fn, {"label": 10})
features = Features({"id": Value("int64"), "label": Value("int64")})
dataset = IterableDataset(ex_iterable, info=DatasetInfo(features=features))
new_features = Features({"id": Value("int64"), "label": Value("bool")})
casted_dataset = dataset.cast(new_features)
assert list(casted_dataset) == [new_features.encode_example(ex) for _, ex in ex_iterable]
def test_iterable_dataset_map_complex_features(dataset: IterableDataset, generate_examples_fn):
# https://github.com/huggingface/datasets/issues/3505
ex_iterable = ExamplesIterable(generate_examples_fn, {"label": "positive"})
features = Features(
{
"id": Value("int64"),
"label": Value("string"),
}
)
dataset = IterableDataset(ex_iterable, info=DatasetInfo(features=features))
dataset = dataset.cast_column("label", ClassLabel(names=["negative", "positive"]))
dataset = dataset.map(lambda x: {"id+1": x["id"] + 1, **x})
assert isinstance(dataset._ex_iterable, MappedExamplesIterable)
features["label"] = ClassLabel(names=["negative", "positive"])
assert [{k: v for k, v in ex.items() if k != "id+1"} for ex in dataset] == [
features.encode_example(ex) for _, ex in ex_iterable
]
