def test_array2d_nonspecific_shape(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
writer = ArrowWriter(features=my_features,
path=os.path.join(tmp_dir, "beta.arrow"))
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 = nlp.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")
def write_flattened_sequence(feats, dummy_data, tmp_dir):
my_features = nlp.Features(feats)
writer = ArrowWriter(features=my_features,
path=os.path.join(tmp_dir, "beta.arrow"))
for key, record in dummy_data:
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
def test_write_schema(self):
fields = {"col_1": pa.string(), "col_2": pa.int64()}
output = pa.BufferOutputStream()
writer = ArrowWriter(stream=output, schema=pa.schema(fields))
writer.write({"col_1": "foo", "col_2": 1})
writer.write({"col_1": "bar", "col_2": 2})
num_examples, num_bytes = writer.finalize()
self.assertEqual(num_examples, 2)
self.assertGreater(num_bytes, 0)
self.assertEqual(writer._schema, pa.schema(fields))
self._check_output(output.getvalue())
def test_compatability_with_string_values(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
my_features["image_id"] = nlp.Value("string")
writer = ArrowWriter(features=my_features,
path=os.path.join(tmp_dir, "beta.arrow"))
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 = nlp.Dataset.from_file(os.path.join(
tmp_dir, "beta.arrow"))
self.assertTrue(isinstance(dataset[0]["image_id"], str),
"image id must be of type string")
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")
writer = ArrowWriter(features=my_features,
path=os.path.join(tmp_dir, "beta.arrow"))
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 = nlp.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")
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)
writer = ArrowWriter(features=my_features,
path=os.path.join(tmp_dir, "beta.arrow"))
my_examples = [
(0, self.get_dict_example_0(shape_1, shape_2)),
(1, self.get_dict_example_1(shape_1, shape_2)),
]
for key, record in my_examples:
example = my_features.encode_example(record)
writer.write(example)
num_examples, num_bytes = writer.finalize()
dataset = nlp.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"])
def test_multiple_extensions_same_row(self):
with tempfile.TemporaryDirectory() as tmp_dir:
my_features = DEFAULT_FEATURES.copy()
writer = ArrowWriter(features=my_features,
path=os.path.join(tmp_dir, "beta.arrow"))
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 = nlp.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)
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")