def test_datasetdict_from_csv(split, features, keep_in_memory, csv_path,
tmp_path):
if split:
path = {split: csv_path}
else:
split = "train"
path = {"train": csv_path, "test": csv_path}
cache_dir = tmp_path / "cache"
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
default_expected_features = {
"col_1": "int64",
"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 else None
with assert_arrow_memory_increases(
) if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = DatasetDict.from_csv(path,
features=features,
cache_dir=cache_dir,
keep_in_memory=keep_in_memory)
assert isinstance(dataset, DatasetDict)
dataset = dataset[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
assert dataset.split == split
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def test_datasetdict_from_csv_keep_in_memory(keep_in_memory, csv_path,
tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {
"col_1": "int64",
"col_2": "int64",
"col_3": "float64"
}
with assert_arrow_memory_increases(
) if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = DatasetDict.from_csv({"train": csv_path},
cache_dir=cache_dir,
keep_in_memory=keep_in_memory)
_check_csv_datasetdict(dataset, expected_features)
def test_datasetdict_from_csv_split(split, csv_path, tmp_path):
if split:
path = {split: csv_path}
else:
split = "train"
path = {"train": csv_path, "test": csv_path}
cache_dir = tmp_path / "cache"
expected_features = {
"col_1": "int64",
"col_2": "int64",
"col_3": "float64"
}
dataset = DatasetDict.from_csv(path, cache_dir=cache_dir)
_check_csv_datasetdict(dataset,
expected_features,
splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
def test_datasetdict_from_csv_features(features, csv_path, tmp_path):
cache_dir = tmp_path / "cache"
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
default_expected_features = {
"col_1": "int64",
"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_csv({"train": csv_path},
features=features,
cache_dir=cache_dir)
_check_csv_datasetdict(dataset, expected_features)