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import random
import tempfile
import unittest
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from absl.testing import parameterized
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import Array2D, Array3D, Array4D, Array5D, Value
from datasets.features.features import Array3DExtensionType, PandasArrayExtensionDtype, _ArrayXD
from datasets.formatting.formatting import NumpyArrowExtractor, SimpleArrowExtractor
SHAPE_TEST_1 = (30, 487)
SHAPE_TEST_2 = (36, 1024)
SHAPE_TEST_3 = (None, 100)
SPEED_TEST_SHAPE = (100, 100)
SPEED_TEST_N_EXAMPLES = 100
DEFAULT_FEATURES = datasets.Features(
{
"text": Array2D(SHAPE_TEST_1, dtype="float32"),
"image": Array2D(SHAPE_TEST_2, dtype="float32"),
"dynamic": Array2D(SHAPE_TEST_3, dtype="float32"),
}
)
def generate_examples(features: dict, num_examples=100, seq_shapes=None):
dummy_data = []
seq_shapes = seq_shapes or {}
for i in range(num_examples):
example = {}
for col_id, (k, v) in enumerate(features.items()):
if isinstance(v, _ArrayXD):
if k == "dynamic":
first_dim = random.randint(1, 3)
data = np.random.rand(first_dim, *v.shape[1:]).astype(v.dtype)
else:
data = np.random.rand(*v.shape).astype(v.dtype)
elif isinstance(v, datasets.Value):
data = "foo"
elif isinstance(v, datasets.Sequence):
while isinstance(v, datasets.Sequence):
v = v.feature
shape = seq_shapes[k]
data = np.random.rand(*shape).astype(v.dtype)
example[k] = data
dummy_data.append((i, example))
return dummy_data
class ExtensionTypeCompatibilityTest(unittest.TestCase):
def test_array2d_nonspecific_shape(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in generate_examples(
features=my_features,
num_examples=1,
):
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
dataset.set_format("numpy")
row = dataset[0]
first_shape = row["image"].shape
second_shape = row["text"].shape
self.assertTrue(first_shape is not None and second_shape is not None, "need atleast 2 different shapes")
self.assertEqual(len(first_shape), len(second_shape), "both shapes are supposed to be equal length")
self.assertNotEqual(first_shape, second_shape, "shapes must not be the same")
del dataset
def test_multiple_extensions_same_row(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in generate_examples(features=my_features, num_examples=1):
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
dataset.set_format("numpy")
row = dataset[0]
first_len = len(row["image"].shape)
second_len = len(row["text"].shape)
third_len = len(row["dynamic"].shape)
self.assertEqual(first_len, 2, "use a sequence type if dim is < 2")
self.assertEqual(second_len, 2, "use a sequence type if dim is < 2")
self.assertEqual(third_len, 2, "use a sequence type if dim is < 2")
del dataset
def test_compatability_with_string_values(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
my_features["image_id"] = datasets.Value("string")
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in generate_examples(features=my_features, num_examples=1):
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
self.assertIsInstance(dataset[0]["image_id"], str, "image id must be of type string")
del dataset
def test_extension_indexing(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
my_features["explicit_ext"] = Array2D((3, 3), dtype="float32")
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in generate_examples(features=my_features, num_examples=1):
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
dataset.set_format("numpy")
data = dataset[0]["explicit_ext"]
self.assertIsInstance(data, np.ndarray, "indexed extension must return numpy.ndarray")
del dataset
def get_array_feature_types():
shape_1 = [3] * 5
shape_2 = [3, 4, 5, 6, 7]
return [
{
"testcase_name": f"{d}d",
"array_feature": array_feature,
"shape_1": tuple(shape_1[:d]),
"shape_2": tuple(shape_2[:d]),
}
for d, array_feature in zip(range(2, 6), [Array2D, Array3D, Array4D, Array5D])
]
@parameterized.named_parameters(get_array_feature_types())
class ArrayXDTest(unittest.TestCase):
def get_features(self, array_feature, shape_1, shape_2):
return datasets.Features(
{
"image": array_feature(shape_1, dtype="float32"),
"source": Value("string"),
"matrix": array_feature(shape_2, dtype="float32"),
}
)
def get_dict_example_0(self, shape_1, shape_2):
return {
"image": np.random.rand(*shape_1).astype("float32"),
"source": "foo",
"matrix": np.random.rand(*shape_2).astype("float32"),
}
def get_dict_example_1(self, shape_1, shape_2):
return {
"image": np.random.rand(*shape_1).astype("float32"),
"matrix": np.random.rand(*shape_2).astype("float32"),
"source": "bar",
}
def get_dict_examples(self, shape_1, shape_2):
return {
"image": np.random.rand(2, *shape_1).astype("float32").tolist(),
"source": ["foo", "bar"],
"matrix": np.random.rand(2, *shape_2).astype("float32").tolist(),
}
def _check_getitem_output_type(self, dataset, shape_1, shape_2, first_matrix):
matrix_column = dataset["matrix"]
self.assertIsInstance(matrix_column, list)
self.assertIsInstance(matrix_column[0], list)
self.assertIsInstance(matrix_column[0][0], list)
self.assertTupleEqual(np.array(matrix_column).shape, (2, *shape_2))
matrix_field_of_first_example = dataset[0]["matrix"]
self.assertIsInstance(matrix_field_of_first_example, list)
self.assertIsInstance(matrix_field_of_first_example, list)
self.assertEqual(np.array(matrix_field_of_first_example).shape, shape_2)
np.testing.assert_array_equal(np.array(matrix_field_of_first_example), np.array(first_matrix))
matrix_field_of_first_two_examples = dataset[:2]["matrix"]
self.assertIsInstance(matrix_field_of_first_two_examples, list)
self.assertIsInstance(matrix_field_of_first_two_examples[0], list)
self.assertIsInstance(matrix_field_of_first_two_examples[0][0], list)
self.assertTupleEqual(np.array(matrix_field_of_first_two_examples).shape, (2, *shape_2))
with dataset.formatted_as("numpy"):
self.assertTupleEqual(dataset["matrix"].shape, (2, *shape_2))
self.assertEqual(dataset[0]["matrix"].shape, shape_2)
self.assertTupleEqual(dataset[:2]["matrix"].shape, (2, *shape_2))
with dataset.formatted_as("pandas"):
self.assertIsInstance(dataset["matrix"], pd.Series)
self.assertIsInstance(dataset[0]["matrix"], pd.Series)
self.assertIsInstance(dataset[:2]["matrix"], pd.Series)
self.assertTupleEqual(dataset["matrix"].to_numpy().shape, (2, *shape_2))
self.assertTupleEqual(dataset[0]["matrix"].to_numpy().shape, (1, *shape_2))
self.assertTupleEqual(dataset[:2]["matrix"].to_numpy().shape, (2, *shape_2))
def test_write(self, array_feature, shape_1, shape_2):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = self.get_features(array_feature, shape_1, shape_2)
my_examples = [
(0, self.get_dict_example_0(shape_1, shape_2)),
(1, self.get_dict_example_1(shape_1, shape_2)),
]
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
for key, record in my_examples:
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
self._check_getitem_output_type(dataset, shape_1, shape_2, my_examples[0][1]["matrix"])
del dataset
def test_write_batch(self, array_feature, shape_1, shape_2):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = self.get_features(array_feature, shape_1, shape_2)
dict_examples = self.get_dict_examples(shape_1, shape_2)
dict_examples = my_features.encode_batch(dict_examples)
with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer:
writer.write_batch(dict_examples)
num_examples, num_bytes = writer.finalize()
dataset = datasets.Dataset.from_file(os.path.join(tmp_dir, "beta.arrow"))
self._check_getitem_output_type(dataset, shape_1, shape_2, dict_examples["matrix"][0])
del dataset
def test_from_dict(self, array_feature, shape_1, shape_2):
dict_examples = self.get_dict_examples(shape_1, shape_2)
dataset = datasets.Dataset.from_dict(
dict_examples, features=self.get_features(array_feature, shape_1, shape_2)
)
self._check_getitem_output_type(dataset, shape_1, shape_2, dict_examples["matrix"][0])
del dataset
class ArrayXDDynamicTest(unittest.TestCase):
def get_one_col_dataset(self, first_dim_list, fixed_shape):
features = datasets.Features({"image": Array3D(shape=(None, *fixed_shape), dtype="float32")})
dict_values = {"image": [np.random.rand(fdim, *fixed_shape).astype("float32") for fdim in first_dim_list]}
dataset = datasets.Dataset.from_dict(dict_values, features=features)
return dataset
def get_two_col_datasset(self, first_dim_list, fixed_shape):
features = datasets.Features(
{"image": Array3D(shape=(None, *fixed_shape), dtype="float32"), "text": Value("string")}
)
dict_values = {
"image": [np.random.rand(fdim, *fixed_shape).astype("float32") for fdim in first_dim_list],
"text": ["text" for _ in first_dim_list],
}
dataset = datasets.Dataset.from_dict(dict_values, features=features)
return dataset
def test_to_pylist(self):
fixed_shape = (2, 2)
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
arr_xd = SimpleArrowExtractor().extract_column(dataset._data)
self.assertIsInstance(arr_xd.type, Array3DExtensionType)
pylist = arr_xd.to_pylist()
for first_dim, single_arr in zip(first_dim_list, pylist):
self.assertIsInstance(single_arr, list)
self.assertTupleEqual(np.array(single_arr).shape, (first_dim, *fixed_shape))
def test_to_numpy(self):
fixed_shape = (2, 2)
# ragged
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
arr_xd = SimpleArrowExtractor().extract_column(dataset._data)
self.assertIsInstance(arr_xd.type, Array3DExtensionType)
# replace with arr_xd = arr_xd.combine_chunks() when 12.0.0 will be the minimal required PyArrow version
arr_xd = arr_xd.type.wrap_array(pa.concat_arrays([chunk.storage for chunk in arr_xd.chunks]))
numpy_arr = arr_xd.to_numpy()
self.assertIsInstance(numpy_arr, np.ndarray)
self.assertEqual(numpy_arr.dtype, object)
for first_dim, single_arr in zip(first_dim_list, numpy_arr):
self.assertIsInstance(single_arr, np.ndarray)
self.assertTupleEqual(single_arr.shape, (first_dim, *fixed_shape))
# non-ragged
first_dim_list = [4, 4, 4]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
arr_xd = SimpleArrowExtractor().extract_column(dataset._data)
self.assertIsInstance(arr_xd.type, Array3DExtensionType)
# replace with arr_xd = arr_xd.combine_chunks() when 12.0.0 will be the minimal required PyArrow version
arr_xd = arr_xd.type.wrap_array(pa.concat_arrays([chunk.storage for chunk in arr_xd.chunks]))
numpy_arr = arr_xd.to_numpy()
self.assertIsInstance(numpy_arr, np.ndarray)
self.assertNotEqual(numpy_arr.dtype, object)
for first_dim, single_arr in zip(first_dim_list, numpy_arr):
self.assertIsInstance(single_arr, np.ndarray)
self.assertTupleEqual(single_arr.shape, (first_dim, *fixed_shape))
def test_iter_dataset(self):
fixed_shape = (2, 2)
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
for first_dim, ds_row in zip(first_dim_list, dataset):
single_arr = ds_row["image"]
self.assertIsInstance(single_arr, list)
self.assertTupleEqual(np.array(single_arr).shape, (first_dim, *fixed_shape))
def test_to_pandas(self):
fixed_shape = (2, 2)
# ragged
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
df = dataset.to_pandas()
self.assertEqual(type(df.image.dtype), PandasArrayExtensionDtype)
numpy_arr = df.image.to_numpy()
self.assertIsInstance(numpy_arr, np.ndarray)
self.assertEqual(numpy_arr.dtype, object)
for first_dim, single_arr in zip(first_dim_list, numpy_arr):
self.assertIsInstance(single_arr, np.ndarray)
self.assertTupleEqual(single_arr.shape, (first_dim, *fixed_shape))
# non-ragged
first_dim_list = [4, 4, 4]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
df = dataset.to_pandas()
self.assertEqual(type(df.image.dtype), PandasArrayExtensionDtype)
numpy_arr = df.image.to_numpy()
self.assertIsInstance(numpy_arr, np.ndarray)
self.assertNotEqual(numpy_arr.dtype, object)
for first_dim, single_arr in zip(first_dim_list, numpy_arr):
self.assertIsInstance(single_arr, np.ndarray)
self.assertTupleEqual(single_arr.shape, (first_dim, *fixed_shape))
def test_map_dataset(self):
fixed_shape = (2, 2)
first_dim_list = [1, 3, 10]
dataset = self.get_one_col_dataset(first_dim_list, fixed_shape)
dataset = dataset.map(lambda a: {"image": np.concatenate([a] * 2)}, input_columns="image")
# check also if above function resulted with 2x bigger first dim
for first_dim, ds_row in zip(first_dim_list, dataset):
single_arr = ds_row["image"]
self.assertIsInstance(single_arr, list)
self.assertTupleEqual(np.array(single_arr).shape, (first_dim * 2, *fixed_shape))
@pytest.mark.parametrize("dtype, dummy_value", [("int32", 1), ("bool", True), ("float64", 1)])
def test_table_to_pandas(dtype, dummy_value):
features = datasets.Features({"foo": datasets.Array2D(dtype=dtype, shape=(2, 2))})
dataset = datasets.Dataset.from_dict({"foo": [[[dummy_value] * 2] * 2]}, features=features)
df = dataset._data.to_pandas()
assert type(df.foo.dtype) == PandasArrayExtensionDtype
arr = df.foo.to_numpy()
np.testing.assert_equal(arr, np.array([[[dummy_value] * 2] * 2], dtype=np.dtype(dtype)))
@pytest.mark.parametrize("dtype, dummy_value", [("int32", 1), ("bool", True), ("float64", 1)])
def test_array_xd_numpy_arrow_extractor(dtype, dummy_value):
features = datasets.Features({"foo": datasets.Array2D(dtype=dtype, shape=(2, 2))})
dataset = datasets.Dataset.from_dict({"foo": [[[dummy_value] * 2] * 2]}, features=features)
arr = NumpyArrowExtractor().extract_column(dataset._data)
assert isinstance(arr, np.ndarray)
np.testing.assert_equal(arr, np.array([[[dummy_value] * 2] * 2], dtype=np.dtype(dtype)))
def test_array_xd_with_none():
# Fixed shape
features = datasets.Features({"foo": datasets.Array2D(dtype="int32", shape=(2, 2))})
dummy_array = np.array([[1, 2], [3, 4]], dtype="int32")
dataset = datasets.Dataset.from_dict({"foo": [dummy_array, None, dummy_array, None]}, features=features)
arr = NumpyArrowExtractor().extract_column(dataset._data)
assert isinstance(arr, np.ndarray) and arr.dtype == np.float64 and arr.shape == (4, 2, 2)
assert np.allclose(arr[0], dummy_array) and np.allclose(arr[2], dummy_array)
assert np.all(np.isnan(arr[1])) and np.all(np.isnan(arr[3])) # broadcasted np.nan - use np.all
# Dynamic shape
features = datasets.Features({"foo": datasets.Array2D(dtype="int32", shape=(None, 2))})
dummy_array = np.array([[1, 2], [3, 4]], dtype="int32")
dataset = datasets.Dataset.from_dict({"foo": [dummy_array, None, dummy_array, None]}, features=features)
arr = NumpyArrowExtractor().extract_column(dataset._data)
assert isinstance(arr, np.ndarray) and arr.dtype == object and arr.shape == (4,)
np.testing.assert_equal(arr[0], dummy_array)
np.testing.assert_equal(arr[2], dummy_array)
assert np.isnan(arr[1]) and np.isnan(arr[3]) # a single np.nan value - np.all not needed
@pytest.mark.parametrize("seq_type", ["no_sequence", "sequence", "sequence_of_sequence"])
@pytest.mark.parametrize(
"dtype",
[
"bool",
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
"float16",
"float32",
"float64",
],
)
@pytest.mark.parametrize("shape, feature_class", [((2, 3), datasets.Array2D), ((2, 3, 4), datasets.Array3D)])
def test_array_xd_with_np(seq_type, dtype, shape, feature_class):
feature = feature_class(dtype=dtype, shape=shape)
data = np.zeros(shape, dtype=dtype)
expected = data.tolist()
if seq_type == "sequence":
feature = datasets.Sequence(feature)
data = [data]
expected = [expected]
elif seq_type == "sequence_of_sequence":
feature = datasets.Sequence(datasets.Sequence(feature))
data = [[data]]
expected = [[expected]]
ds = datasets.Dataset.from_dict({"col": [data]}, features=datasets.Features({"col": feature}))
assert ds[0]["col"] == expected
@pytest.mark.parametrize("with_none", [False, True])
def test_dataset_map(with_none):
ds = datasets.Dataset.from_dict({"path": ["path1", "path2"]})
def process_data(batch):
batch = {
"image": [
np.array(
[
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
[[10, 20, 30], [40, 50, 60], [70, 80, 90]],
[[100, 200, 300], [400, 500, 600], [700, 800, 900]],
]
)
for _ in batch["path"]
]
}
if with_none:
batch["image"][0] = None
return batch
features = datasets.Features({"image": Array3D(dtype="int32", shape=(3, 3, 3))})
processed_ds = ds.map(process_data, batched=True, remove_columns=ds.column_names, features=features)
assert processed_ds.shape == (2, 1)
with processed_ds.with_format("numpy") as pds:
for i, example in enumerate(pds):
assert "image" in example
assert isinstance(example["image"], np.ndarray)
assert example["image"].shape == (3, 3, 3)
if with_none and i == 0:
assert np.all(np.isnan(example["image"]))
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