def test_datasetdict_from_parquet_keep_in_memory(keep_in_memory, parquet_path,
tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {
"col_1": "string",
"col_2": "int64",
"col_3": "float64"
}
with assert_arrow_memory_increases(
) if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = DatasetDict.from_parquet({"train": parquet_path},
cache_dir=cache_dir,
keep_in_memory=keep_in_memory)
_check_parquet_datasetdict(dataset, expected_features)
def test_datasetdict_from_parquet_split(split, parquet_path, tmp_path):
if split:
path = {split: parquet_path}
else:
split = "train"
path = {"train": parquet_path, "test": parquet_path}
cache_dir = tmp_path / "cache"
expected_features = {
"col_1": "string",
"col_2": "int64",
"col_3": "float64"
}
dataset = DatasetDict.from_parquet(path, cache_dir=cache_dir)
_check_parquet_datasetdict(dataset,
expected_features,
splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
def test_datasetdict_from_parquet_features(features, parquet_path, tmp_path):
cache_dir = tmp_path / "cache"
default_expected_features = {
"col_1": "string",
"col_2": "int64",
"col_3": "float64"
}
expected_features = features.copy(
) if features else default_expected_features
features = (Features({
feature: Value(dtype)
for feature, dtype in features.items()
}) if features is not None else None)
dataset = DatasetDict.from_parquet({"train": parquet_path},
features=features,
cache_dir=cache_dir)
_check_parquet_datasetdict(dataset, expected_features)