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 _prepare_split(self, split_generator, **kwargs):
fname = "{}-{}.arrow".format(self.name, split_generator.name)
writer = ArrowWriter(features=self.info.features,
path=os.path.join(self._cache_dir, fname))
writer.write_batch({"text": ["foo"] * 100})
num_examples, num_bytes = writer.finalize()
split_generator.split_info.num_examples = num_examples
split_generator.split_info.num_bytes = num_bytes
def test_write_batch_schema(self):
fields = {"col_1": pa.string(), "col_2": pa.int64()}
output = pa.BufferOutputStream()
writer = ArrowWriter(stream=output, schema=pa.schema(fields))
writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 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_write_no_schema(self):
output = pa.BufferOutputStream()
writer = ArrowWriter(stream=output)
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)
fields = {"col_1": pa.string(), "col_2": pa.int64()}
self.assertEqual(writer._schema, pa.schema(fields))
self._check_output(output.getvalue())
def test_write_file(self):
with tempfile.TemporaryDirectory() as tmp_dir:
fields = {"col_1": pa.string(), "col_2": pa.int64()}
output = os.path.join(tmp_dir, "test.arrow")
writer = ArrowWriter(path=output, schema=pa.schema(fields))
writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 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)
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_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)
writer = ArrowWriter(features=my_features,
path=os.path.join(tmp_dir, "beta.arrow"))
dict_examples = self.get_dict_examples(shape_1, shape_2)
dict_examples = my_features.encode_batch(dict_examples)
writer.write_batch(dict_examples)
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,
dict_examples["matrix"][0])
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 _init_writer(self):
if self.keep_in_memory:
self.buf_writer = pa.BufferOutputStream()
self.writer = ArrowWriter(features=self.info.features,
stream=self.buf_writer,
writer_batch_size=self.writer_batch_size)
else:
self.buf_writer = None
# Get cache file name and lock it
if self.cache_file_name is None or self.filelock is None:
cache_file_name, filelock = self._create_cache_file()
self.cache_file_name = cache_file_name
self.filelock = filelock
self.writer = ArrowWriter(features=self.info.features,
path=self.cache_file_name,
writer_batch_size=self.writer_batch_size)
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")
def test_as_dataset(self):
with tempfile.TemporaryDirectory() as tmp_dir:
dummy_builder = DummyBuilder(cache_dir=tmp_dir, name="dummy")
os.makedirs(dummy_builder.cache_dir)
dummy_builder.info.splits = SplitDict()
dummy_builder.info.splits.add(SplitInfo("train", num_examples=10))
dummy_builder.info.splits.add(SplitInfo("test", num_examples=10))
for split in dummy_builder.info.splits:
writer = ArrowWriter(
path=os.path.join(dummy_builder.cache_dir,
f"dummy_builder-{split}.arrow"),
features=Features({"text": Value("string")}),
)
writer.write_batch({"text": ["foo"] * 10})
writer.finalize()
dsets = dummy_builder.as_dataset()
self.assertIsInstance(dsets, DatasetDict)
self.assertListEqual(list(dsets.keys()), ["train", "test"])
self.assertEqual(len(dsets["train"]), 10)
self.assertEqual(len(dsets["test"]), 10)
dset = dummy_builder.as_dataset("train")
self.assertIsInstance(dset, Dataset)
self.assertEqual(dset.split, "train")
self.assertEqual(len(dset), 10)
dset = dummy_builder.as_dataset("train+test[:30%]")
self.assertIsInstance(dset, Dataset)
self.assertEqual(dset.split, "train+test[:30%]")
self.assertEqual(len(dset), 13)
def test_as_dataset_with_post_process(self):
def _post_process(self, dataset, resources_paths):
def char_tokenize(example):
return {"tokens": list(example["text"])}
return dataset.map(
char_tokenize,
cache_file_name=resources_paths["tokenized_dataset"])
def _post_processing_resources(self, split):
return {
"tokenized_dataset":
"tokenized_dataset-{split}.arrow".format(split=split)
}
with tempfile.TemporaryDirectory() as tmp_dir:
dummy_builder = DummyBuilder(cache_dir=tmp_dir, name="dummy")
dummy_builder.info.post_processed = PostProcessedInfo(
features=Features({
"text": Value("string"),
"tokens": [Value("string")]
}))
dummy_builder._post_process = types.MethodType(
_post_process, dummy_builder)
dummy_builder._post_processing_resources = types.MethodType(
_post_processing_resources, dummy_builder)
os.makedirs(dummy_builder.cache_dir)
dummy_builder.info.splits = SplitDict()
dummy_builder.info.splits.add(SplitInfo("train", num_examples=10))
dummy_builder.info.splits.add(SplitInfo("test", num_examples=10))
for split in dummy_builder.info.splits:
writer = ArrowWriter(
path=os.path.join(dummy_builder.cache_dir,
f"dummy_builder-{split}.arrow"),
features=Features({"text": Value("string")}),
)
writer.write_batch({"text": ["foo"] * 10})
writer.finalize()
writer = ArrowWriter(
path=os.path.join(dummy_builder.cache_dir,
f"tokenized_dataset-{split}.arrow"),
features=Features({
"text": Value("string"),
"tokens": [Value("string")]
}),
)
writer.write_batch({
"text": ["foo"] * 10,
"tokens": [list("foo")] * 10
})
writer.finalize()
dsets = dummy_builder.as_dataset()
self.assertIsInstance(dsets, DatasetDict)
self.assertListEqual(list(dsets.keys()), ["train", "test"])
self.assertEqual(len(dsets["train"]), 10)
self.assertEqual(len(dsets["test"]), 10)
self.assertDictEqual(
dsets["train"].features,
Features({
"text": Value("string"),
"tokens": [Value("string")]
}))
self.assertDictEqual(
dsets["test"].features,
Features({
"text": Value("string"),
"tokens": [Value("string")]
}))
self.assertListEqual(dsets["train"].column_names,
["text", "tokens"])
self.assertListEqual(dsets["test"].column_names,
["text", "tokens"])
dset = dummy_builder.as_dataset("train")
self.assertIsInstance(dset, Dataset)
self.assertEqual(dset.split, "train")
self.assertEqual(len(dset), 10)
self.assertDictEqual(
dset.features,
Features({
"text": Value("string"),
"tokens": [Value("string")]
}))
self.assertListEqual(dset.column_names, ["text", "tokens"])
self.assertGreater(dummy_builder.info.post_processing_size, 0)
self.assertGreater(
dummy_builder.info.post_processed.resources_checksums["train"]
["tokenized_dataset"]["num_bytes"], 0)
dset = dummy_builder.as_dataset("train+test[:30%]")
self.assertIsInstance(dset, Dataset)
self.assertEqual(dset.split, "train+test[:30%]")
self.assertEqual(len(dset), 13)
self.assertDictEqual(
dset.features,
Features({
"text": Value("string"),
"tokens": [Value("string")]
}))
self.assertListEqual(dset.column_names, ["text", "tokens"])
def _post_process(self, dataset, resources_paths):
return dataset.select([0, 1], keep_in_memory=True)
with tempfile.TemporaryDirectory() as tmp_dir:
dummy_builder = DummyBuilder(cache_dir=tmp_dir, name="dummy")
dummy_builder._post_process = types.MethodType(
_post_process, dummy_builder)
os.makedirs(dummy_builder.cache_dir)
dummy_builder.info.splits = SplitDict()
dummy_builder.info.splits.add(SplitInfo("train", num_examples=10))
dummy_builder.info.splits.add(SplitInfo("test", num_examples=10))
for split in dummy_builder.info.splits:
writer = ArrowWriter(
path=os.path.join(dummy_builder.cache_dir,
f"dummy_builder-{split}.arrow"),
features=Features({"text": Value("string")}),
)
writer.write_batch({"text": ["foo"] * 10})
writer.finalize()
writer = ArrowWriter(
path=os.path.join(dummy_builder.cache_dir,
f"small_dataset-{split}.arrow"),
features=Features({"text": Value("string")}),
)
writer.write_batch({"text": ["foo"] * 2})
writer.finalize()
dsets = dummy_builder.as_dataset()
self.assertIsInstance(dsets, DatasetDict)
self.assertListEqual(list(dsets.keys()), ["train", "test"])
self.assertEqual(len(dsets["train"]), 2)
self.assertEqual(len(dsets["test"]), 2)
self.assertDictEqual(dsets["train"].features,
Features({"text": Value("string")}))
self.assertDictEqual(dsets["test"].features,
Features({"text": Value("string")}))
self.assertListEqual(dsets["train"].column_names, ["text"])
self.assertListEqual(dsets["test"].column_names, ["text"])
dset = dummy_builder.as_dataset("train")
self.assertIsInstance(dset, Dataset)
self.assertEqual(dset.split, "train")
self.assertEqual(len(dset), 2)
self.assertDictEqual(dset.features,
Features({"text": Value("string")}))
self.assertListEqual(dset.column_names, ["text"])
dset = dummy_builder.as_dataset("train+test[:30%]")
self.assertIsInstance(dset, Dataset)
self.assertEqual(dset.split, "train+test[:30%]")
self.assertEqual(len(dset), 2)
self.assertDictEqual(dset.features,
Features({"text": Value("string")}))
self.assertListEqual(dset.column_names, ["text"])
def _post_process(self, dataset, resources_paths):
if os.path.exists(resources_paths["index"]):
dataset.load_faiss_index("my_index", resources_paths["index"])
return dataset
else:
dataset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((len(dataset), 8)),
string_factory="Flat",
index_name="my_index")
dataset.save_faiss_index("my_index", resources_paths["index"])
return dataset
def _post_processing_resources(self, split):
return {"index": "Flat-{split}.faiss".format(split=split)}
with tempfile.TemporaryDirectory() as tmp_dir:
dummy_builder = DummyBuilder(cache_dir=tmp_dir, name="dummy")
dummy_builder._post_process = types.MethodType(
_post_process, dummy_builder)
dummy_builder._post_processing_resources = types.MethodType(
_post_processing_resources, dummy_builder)
os.makedirs(dummy_builder.cache_dir)
dummy_builder.info.splits = SplitDict()
dummy_builder.info.splits.add(SplitInfo("train", num_examples=10))
dummy_builder.info.splits.add(SplitInfo("test", num_examples=10))
for split in dummy_builder.info.splits:
writer = ArrowWriter(
path=os.path.join(dummy_builder.cache_dir,
f"dummy_builder-{split}.arrow"),
features=Features({"text": Value("string")}),
)
writer.write_batch({"text": ["foo"] * 10})
writer.finalize()
writer = ArrowWriter(
path=os.path.join(dummy_builder.cache_dir,
f"small_dataset-{split}.arrow"),
features=Features({"text": Value("string")}),
)
writer.write_batch({"text": ["foo"] * 2})
writer.finalize()
dsets = dummy_builder.as_dataset()
self.assertIsInstance(dsets, DatasetDict)
self.assertListEqual(list(dsets.keys()), ["train", "test"])
self.assertEqual(len(dsets["train"]), 10)
self.assertEqual(len(dsets["test"]), 10)
self.assertDictEqual(dsets["train"].features,
Features({"text": Value("string")}))
self.assertDictEqual(dsets["test"].features,
Features({"text": Value("string")}))
self.assertListEqual(dsets["train"].column_names, ["text"])
self.assertListEqual(dsets["test"].column_names, ["text"])
self.assertListEqual(dsets["train"].list_indexes(), ["my_index"])
self.assertListEqual(dsets["test"].list_indexes(), ["my_index"])
self.assertGreater(dummy_builder.info.post_processing_size, 0)
self.assertGreater(
dummy_builder.info.post_processed.resources_checksums["train"]
["index"]["num_bytes"], 0)
dset = dummy_builder.as_dataset("train")
self.assertIsInstance(dset, Dataset)
self.assertEqual(dset.split, "train")
self.assertEqual(len(dset), 10)
self.assertDictEqual(dset.features,
Features({"text": Value("string")}))
self.assertListEqual(dset.column_names, ["text"])
self.assertListEqual(dset.list_indexes(), ["my_index"])
dset = dummy_builder.as_dataset("train+test[:30%]")
self.assertIsInstance(dset, Dataset)
self.assertEqual(dset.split, "train+test[:30%]")
self.assertEqual(len(dset), 13)
self.assertDictEqual(dset.features,
Features({"text": Value("string")}))
self.assertListEqual(dset.column_names, ["text"])
self.assertListEqual(dset.list_indexes(), ["my_index"])