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test_dataset_common.py
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test_dataset_common.py
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# coding=utf-8
# Copyright 2020 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import os
import tempfile
import warnings
from functools import wraps
from multiprocessing import Pool
from unittest import TestCase
from absl.testing import parameterized
from datasets import (
BuilderConfig,
DatasetBuilder,
DownloadConfig,
Features,
GenerateMode,
MockDownloadManager,
Value,
cached_path,
hf_api,
import_main_class,
load_dataset,
logging,
prepare_module,
)
from datasets.search import _has_faiss
from datasets.utils.file_utils import is_remote_url
from .utils import for_all_test_methods, local, remote, slow
logger = logging.get_logger(__name__)
REQUIRE_FAISS = {"wiki_dpr"}
def skip_if_dataset_requires_faiss(test_case):
@wraps(test_case)
def wrapper(self, dataset_name):
if not _has_faiss and dataset_name in REQUIRE_FAISS:
self.skipTest('"test requires Faiss"')
else:
test_case(self, dataset_name)
return wrapper
def skip_if_not_compatible_with_windows(test_case):
if os.name == "nt": # windows
@wraps(test_case)
def wrapper(self, dataset_name):
try:
test_case(self, dataset_name)
except FileNotFoundError as e:
if "[WinError 206]" in str(e): # if there's a path that exceeds windows' 256 characters limit
warnings.warn("test not compatible with windows ([WinError 206] error)")
self.skipTest('"test not compatible with windows ([WinError 206] error)"')
else:
raise
return wrapper
else:
return test_case
class DatasetTester(object):
def __init__(self, parent):
self.parent = parent if parent is not None else TestCase()
def load_builder_class(self, dataset_name, is_local=False):
# Download/copy dataset script
if is_local is True:
module_path, _ = prepare_module("./datasets/" + dataset_name)
else:
module_path, _ = prepare_module(dataset_name, download_config=DownloadConfig(force_download=True))
# Get dataset builder class
builder_cls = import_main_class(module_path)
return builder_cls
def load_all_configs(self, dataset_name, is_local=False):
# get builder class
builder_cls = self.load_builder_class(dataset_name, is_local=is_local)
builder = builder_cls
if len(builder.BUILDER_CONFIGS) == 0:
return [None]
return builder.BUILDER_CONFIGS
def check_load_dataset(self, dataset_name, configs, is_local=False):
for config in configs:
with tempfile.TemporaryDirectory() as processed_temp_dir, tempfile.TemporaryDirectory() as raw_temp_dir:
# create config and dataset
dataset_builder_cls = self.load_builder_class(dataset_name, is_local=is_local)
name = config.name if config is not None else None
dataset_builder = dataset_builder_cls(name=name, cache_dir=processed_temp_dir)
# TODO: skip Beam datasets and datasets that lack dummy data for now
if not dataset_builder.test_dummy_data:
logger.info("Skip tests for this dataset for now")
return
if config is not None:
version = config.version
else:
version = dataset_builder.VERSION
def check_if_url_is_valid(url):
if is_remote_url(url) and "\\" in url:
raise ValueError(f"Bad remote url '{url}'' since it contains a backslash")
# create mock data loader manager that has a special download_and_extract() method to download dummy data instead of real data
mock_dl_manager = MockDownloadManager(
dataset_name=dataset_name,
config=config,
version=version,
cache_dir=raw_temp_dir,
is_local=is_local,
download_callbacks=[check_if_url_is_valid],
)
if dataset_builder.__class__.__name__ == "Csv":
# need slight adoption for csv dataset
mock_dl_manager.download_dummy_data()
path_to_dummy_data = mock_dl_manager.dummy_file
dataset_builder.config.data_files = {
"train": os.path.join(path_to_dummy_data, "train.csv"),
"test": os.path.join(path_to_dummy_data, "test.csv"),
"dev": os.path.join(path_to_dummy_data, "dev.csv"),
}
elif dataset_builder.__class__.__name__ == "Json":
# need slight adoption for json dataset
mock_dl_manager.download_dummy_data()
path_to_dummy_data = mock_dl_manager.dummy_file
dataset_builder.config.data_files = {
"train": os.path.join(path_to_dummy_data, "train.json"),
"test": os.path.join(path_to_dummy_data, "test.json"),
"dev": os.path.join(path_to_dummy_data, "dev.json"),
}
elif dataset_builder.__class__.__name__ == "Pandas":
# need slight adoption for json dataset
mock_dl_manager.download_dummy_data()
path_to_dummy_data = mock_dl_manager.dummy_file
dataset_builder.config.data_files = {
"train": os.path.join(path_to_dummy_data, "train.pkl"),
"test": os.path.join(path_to_dummy_data, "test.pkl"),
"dev": os.path.join(path_to_dummy_data, "dev.pkl"),
}
elif dataset_builder.__class__.__name__ == "Text":
mock_dl_manager.download_dummy_data()
path_to_dummy_data = mock_dl_manager.dummy_file
dataset_builder.config.data_files = {
"train": os.path.join(path_to_dummy_data, "train.txt"),
"test": os.path.join(path_to_dummy_data, "test.txt"),
"dev": os.path.join(path_to_dummy_data, "dev.txt"),
}
# mock size needed for dummy data instead of actual dataset
if dataset_builder.info is not None:
# approximate upper bound of order of magnitude of dummy data files
one_mega_byte = 2 << 19
dataset_builder.info.size_in_bytes = 2 * one_mega_byte
dataset_builder.info.download_size = one_mega_byte
dataset_builder.info.dataset_size = one_mega_byte
# generate examples from dummy data
dataset_builder.download_and_prepare(
dl_manager=mock_dl_manager,
download_mode=GenerateMode.FORCE_REDOWNLOAD,
ignore_verifications=True,
try_from_hf_gcs=False,
)
# get dataset
dataset = dataset_builder.as_dataset(ignore_verifications=True)
# check that dataset is not empty
self.parent.assertListEqual(sorted(dataset_builder.info.splits.keys()), sorted(dataset))
for split in dataset_builder.info.splits.keys():
# check that loaded datset is not empty
self.parent.assertTrue(len(dataset[split]) > 0)
del dataset
def get_local_dataset_names():
datasets = [dataset_dir.split(os.sep)[-2] for dataset_dir in glob.glob("./datasets/*/")]
return [{"testcase_name": x, "dataset_name": x} for x in datasets]
@parameterized.named_parameters(get_local_dataset_names())
@for_all_test_methods(skip_if_dataset_requires_faiss, skip_if_not_compatible_with_windows)
@local
class LocalDatasetTest(parameterized.TestCase):
dataset_name = None
def setUp(self):
self.dataset_tester = DatasetTester(self)
def test_load_dataset(self, dataset_name):
configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]
self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True)
def test_builder_class(self, dataset_name):
builder_cls = self.dataset_tester.load_builder_class(dataset_name, is_local=True)
name = builder_cls.BUILDER_CONFIGS[0].name if builder_cls.BUILDER_CONFIGS else None
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = builder_cls(name=name, cache_dir=tmp_cache_dir)
self.assertTrue(isinstance(builder, DatasetBuilder))
def test_builder_configs(self, dataset_name):
builder_configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)
self.assertTrue(len(builder_configs) > 0)
if builder_configs[0] is not None:
all(self.assertTrue(isinstance(config, BuilderConfig)) for config in builder_configs)
@slow
def test_load_dataset_all_configs(self, dataset_name):
configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)
self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True)
@slow
def test_load_real_dataset(self, dataset_name):
path = "./datasets/" + dataset_name
module_path, hash = prepare_module(path, download_config=DownloadConfig(local_files_only=True), dataset=True)
builder_cls = import_main_class(module_path, dataset=True)
name = builder_cls.BUILDER_CONFIGS[0].name if builder_cls.BUILDER_CONFIGS else None
with tempfile.TemporaryDirectory() as temp_cache_dir:
dataset = load_dataset(
path, name=name, cache_dir=temp_cache_dir, download_mode=GenerateMode.FORCE_REDOWNLOAD
)
for split in dataset.keys():
self.assertTrue(len(dataset[split]) > 0)
del dataset
@slow
def test_load_real_dataset_all_configs(self, dataset_name):
path = "./datasets/" + dataset_name
module_path, hash = prepare_module(path, download_config=DownloadConfig(local_files_only=True), dataset=True)
builder_cls = import_main_class(module_path, dataset=True)
config_names = (
[config.name for config in builder_cls.BUILDER_CONFIGS] if len(builder_cls.BUILDER_CONFIGS) > 0 else [None]
)
for name in config_names:
with tempfile.TemporaryDirectory() as temp_cache_dir:
dataset = load_dataset(
path, name=name, cache_dir=temp_cache_dir, download_mode=GenerateMode.FORCE_REDOWNLOAD
)
for split in dataset.keys():
self.assertTrue(len(dataset[split]) > 0)
del dataset
def distributed_load_dataset(args):
data_name, tmp_dir, datafiles = args
dataset = load_dataset(data_name, cache_dir=tmp_dir, data_files=datafiles)
return dataset
class DistributedDatasetTest(TestCase):
def test_load_dataset_distributed(self):
num_workers = 5
with tempfile.TemporaryDirectory() as tmp_dir:
data_name = "./datasets/csv"
data_base_path = os.path.join(data_name, "dummy/0.0.0/dummy_data.zip")
local_path = cached_path(
data_base_path, cache_dir=tmp_dir, extract_compressed_file=True, force_extract=True
)
datafiles = {
"train": os.path.join(local_path, "dummy_data/train.csv"),
"dev": os.path.join(local_path, "dummy_data/dev.csv"),
"test": os.path.join(local_path, "dummy_data/test.csv"),
}
args = data_name, tmp_dir, datafiles
with Pool(processes=num_workers) as pool: # start num_workers processes
result = pool.apply_async(distributed_load_dataset, (args,))
dataset = result.get(timeout=20)
del result, dataset
datasets = pool.map(distributed_load_dataset, [args] * num_workers)
for _ in range(len(datasets)):
dataset = datasets.pop()
del dataset
def get_remote_dataset_names():
api = hf_api.HfApi()
# fetch all dataset names
datasets = api.dataset_list(with_community_datasets=False, id_only=True)
return [{"testcase_name": x, "dataset_name": x} for x in datasets]
@parameterized.named_parameters(get_remote_dataset_names())
@for_all_test_methods(skip_if_dataset_requires_faiss, skip_if_not_compatible_with_windows)
@remote
class RemoteDatasetTest(parameterized.TestCase):
dataset_name = None
def setUp(self):
self.dataset_tester = DatasetTester(self)
def test_builder_class(self, dataset_name):
builder_cls = self.dataset_tester.load_builder_class(dataset_name)
name = builder_cls.BUILDER_CONFIGS[0].name if builder_cls.BUILDER_CONFIGS else None
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = builder_cls(name=name, cache_dir=tmp_cache_dir)
self.assertTrue(isinstance(builder, DatasetBuilder))
def test_builder_configs(self, dataset_name):
builder_configs = self.dataset_tester.load_all_configs(dataset_name)
self.assertTrue(len(builder_configs) > 0)
if builder_configs[0] is not None:
all(self.assertTrue(isinstance(config, BuilderConfig)) for config in builder_configs)
def test_load_dataset(self, dataset_name):
configs = self.dataset_tester.load_all_configs(dataset_name)[:1]
self.dataset_tester.check_load_dataset(dataset_name, configs)
@slow
def test_load_real_dataset(self, dataset_name):
path = dataset_name
module_path, hash = prepare_module(path, download_config=DownloadConfig(force_download=True), dataset=True)
builder_cls = import_main_class(module_path, dataset=True)
name = builder_cls.BUILDER_CONFIGS[0].name if builder_cls.BUILDER_CONFIGS else None
with tempfile.TemporaryDirectory() as temp_cache_dir:
dataset = load_dataset(
path, name=name, cache_dir=temp_cache_dir, download_mode=GenerateMode.FORCE_REDOWNLOAD
)
for split in dataset.keys():
self.assertTrue(len(dataset[split]) > 0)
del dataset
@slow
def test_load_real_dataset_all_configs(self, dataset_name):
path = dataset_name
module_path, hash = prepare_module(path, download_config=DownloadConfig(force_download=True), dataset=True)
builder_cls = import_main_class(module_path, dataset=True)
config_names = (
[config.name for config in builder_cls.BUILDER_CONFIGS] if len(builder_cls.BUILDER_CONFIGS) > 0 else [None]
)
for name in config_names:
with tempfile.TemporaryDirectory() as temp_cache_dir:
dataset = load_dataset(
path, name=name, cache_dir=temp_cache_dir, download_mode=GenerateMode.FORCE_REDOWNLOAD
)
for split in dataset.keys():
self.assertTrue(len(dataset[split]) > 0)
del dataset
class TextTest(TestCase):
def test_caching(self):
n_samples = 10
with tempfile.TemporaryDirectory() as tmp_dir:
# Use \n for newline. Windows automatically adds the \r when writing the file
# see https://docs.python.org/3/library/os.html#os.linesep
open(os.path.join(tmp_dir, "text.txt"), "w", encoding="utf-8").write(
"\n".join("foo" for _ in range(n_samples))
)
ds = load_dataset(
"./datasets/text", data_files=os.path.join(tmp_dir, "text.txt"), cache_dir=tmp_dir, split="train"
)
data_file = ds._data_files[0]
fingerprint = ds._fingerprint
self.assertEqual(len(ds), n_samples)
del ds
ds = load_dataset(
"./datasets/text", data_files=os.path.join(tmp_dir, "text.txt"), cache_dir=tmp_dir, split="train"
)
self.assertEqual(ds._data_files[0], data_file)
self.assertEqual(ds._fingerprint, fingerprint)
del ds
open(os.path.join(tmp_dir, "text.txt"), "w", encoding="utf-8").write(
"\n".join("bar" for _ in range(n_samples))
)
ds = load_dataset(
"./datasets/text", data_files=os.path.join(tmp_dir, "text.txt"), cache_dir=tmp_dir, split="train"
)
self.assertNotEqual(ds._data_files[0], data_file)
self.assertNotEqual(ds._fingerprint, fingerprint)
self.assertEqual(len(ds), n_samples)
del ds
class CsvTest(TestCase):
def test_caching(self):
n_rows = 10
features = Features({"foo": Value("string"), "bar": Value("string")})
with tempfile.TemporaryDirectory() as tmp_dir:
# Use \n for newline. Windows automatically adds the \r when writing the file
# see https://docs.python.org/3/library/os.html#os.linesep
open(os.path.join(tmp_dir, "table.csv"), "w", encoding="utf-8").write(
"\n".join(",".join(["foo", "bar"]) for _ in range(n_rows + 1))
)
ds = load_dataset(
"./datasets/csv", data_files=os.path.join(tmp_dir, "table.csv"), cache_dir=tmp_dir, split="train"
)
data_file = ds._data_files[0]
fingerprint = ds._fingerprint
self.assertEqual(len(ds), n_rows)
del ds
ds = load_dataset(
"./datasets/csv", data_files=os.path.join(tmp_dir, "table.csv"), cache_dir=tmp_dir, split="train"
)
self.assertEqual(ds._data_files[0], data_file)
self.assertEqual(ds._fingerprint, fingerprint)
del ds
ds = load_dataset(
"./datasets/csv",
data_files=os.path.join(tmp_dir, "table.csv"),
cache_dir=tmp_dir,
split="train",
features=features,
)
self.assertNotEqual(ds._data_files[0], data_file)
self.assertNotEqual(ds._fingerprint, fingerprint)
del ds
open(os.path.join(tmp_dir, "table.csv"), "w", encoding="utf-8").write(
"\n".join(",".join(["Foo", "Bar"]) for _ in range(n_rows + 1))
)
ds = load_dataset(
"./datasets/csv", data_files=os.path.join(tmp_dir, "table.csv"), cache_dir=tmp_dir, split="train"
)
self.assertNotEqual(ds._data_files[0], data_file)
self.assertNotEqual(ds._fingerprint, fingerprint)
self.assertEqual(len(ds), n_rows)
del ds
def test_sep(self):
n_rows = 10
n_cols = 3
with tempfile.TemporaryDirectory() as tmp_dir:
open(os.path.join(tmp_dir, "table_comma.csv"), "w", encoding="utf-8").write(
"\n".join(",".join([str(i) for i in range(n_cols)]) for _ in range(n_rows + 1))
)
open(os.path.join(tmp_dir, "table_tab.csv"), "w", encoding="utf-8").write(
"\n".join("\t".join([str(i) for i in range(n_cols)]) for _ in range(n_rows + 1))
)
ds = load_dataset(
"./datasets/csv",
data_files=os.path.join(tmp_dir, "table_comma.csv"),
cache_dir=tmp_dir,
split="train",
sep=",",
)
self.assertEqual(len(ds), n_rows)
self.assertEqual(len(ds.column_names), n_cols)
del ds
ds = load_dataset(
"./datasets/csv",
data_files=os.path.join(tmp_dir, "table_tab.csv"),
cache_dir=tmp_dir,
split="train",
sep="\t",
)
self.assertEqual(len(ds), n_rows)
self.assertEqual(len(ds.column_names), n_cols)
del ds
ds = load_dataset(
"./datasets/csv",
data_files=os.path.join(tmp_dir, "table_comma.csv"),
cache_dir=tmp_dir,
split="train",
sep="\t",
)
self.assertEqual(len(ds), n_rows)
self.assertEqual(len(ds.column_names), 1)
del ds
def test_features(self):
n_rows = 10
n_cols = 3
def get_features(type):
return Features({str(i): Value(type) for i in range(n_cols)})
with tempfile.TemporaryDirectory() as tmp_dir:
open(os.path.join(tmp_dir, "table.csv"), "w", encoding="utf-8").write(
"\n".join(",".join([str(i) for i in range(n_cols)]) for _ in range(n_rows + 1))
)
for type in ["float64", "int8"]:
features = get_features(type)
ds = load_dataset(
"./datasets/csv",
data_files=os.path.join(tmp_dir, "table.csv"),
cache_dir=tmp_dir,
split="train",
features=features,
)
self.assertEqual(len(ds), n_rows)
self.assertDictEqual(ds.features, features)
del ds