def test_csv_dataset_reader(path_type, split, features, keep_in_memory,
csv_path, tmp_path):
if issubclass(path_type, str):
path = csv_path
elif issubclass(path_type, list):
path = [csv_path]
cache_dir = tmp_path / "cache"
expected_split = str(split) if split else "train"
# 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 = CsvDatasetReader(path,
split=split,
features=features,
cache_dir=cache_dir,
keep_in_memory=keep_in_memory).read()
assert isinstance(dataset, Dataset)
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
assert dataset.split == expected_split
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def test_dataset_from_csv_path_type(path_type, csv_path, tmp_path):
if issubclass(path_type, str):
path = csv_path
elif issubclass(path_type, list):
path = [csv_path]
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
dataset = CsvDatasetReader(path, cache_dir=cache_dir).read()
_check_csv_dataset(dataset, expected_features)
def test_dataset_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 = CsvDatasetReader(csv_path, features=features, cache_dir=cache_dir).read()
_check_csv_dataset(dataset, expected_features)
def test_dataset_from_csv_split(split, csv_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {
"col_1": "int64",
"col_2": "int64",
"col_3": "float64"
}
dataset = CsvDatasetReader(csv_path, cache_dir=cache_dir,
split=split).read()
_check_csv_dataset(dataset, expected_features)
assert dataset.split == str(split) if split else "train"
def test_dataset_to_csv_multiproc(csv_path, tmp_path):
cache_dir = tmp_path / "cache"
output_csv = os.path.join(cache_dir, "tmp.csv")
dataset = CsvDatasetReader({"train": csv_path}, cache_dir=cache_dir).read()
CsvDatasetWriter(dataset["train"], output_csv, index=False, num_proc=2).write()
original_csv = iter_csv_file(csv_path)
expected_csv = iter_csv_file(output_csv)
for row1, row2 in zip(original_csv, expected_csv):
assert row1 == row2
def test_csv_datasetdict_reader_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 = CsvDatasetReader(path, cache_dir=cache_dir).read()
_check_csv_datasetdict(dataset, expected_features, splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
def test_dataset_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 = CsvDatasetReader(csv_path,
cache_dir=cache_dir,
keep_in_memory=keep_in_memory).read()
_check_csv_dataset(dataset, expected_features)
def test_csv_datasetdict_reader(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
previous_allocated_memory = pa.total_allocated_bytes()
dataset = CsvDatasetReader(path,
features=features,
cache_dir=cache_dir,
keep_in_memory=keep_in_memory).read()
increased_allocated_memory = (pa.total_allocated_bytes() -
previous_allocated_memory) > 0
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
assert increased_allocated_memory == keep_in_memory
def test_dataset_to_csv_invalidproc(csv_path, tmp_path):
cache_dir = tmp_path / "cache"
output_csv = os.path.join(cache_dir, "tmp.csv")
dataset = CsvDatasetReader({"train": csv_path}, cache_dir=cache_dir).read()
with pytest.raises(ValueError):
CsvDatasetWriter(dataset["train"], output_csv, index=False, num_proc=0)