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import os
import tarfile
from collections import defaultdict
from unittest.mock import patch
from torchtext.datasets.yelpreviewfull import YelpReviewFull
from torchtext.datasets.yelpreviewpolarity import YelpReviewPolarity
from ..common.case_utils import TempDirMixin, zip_equal, get_random_unicode
from ..common.parameterized_utils import nested_params
from ..common.torchtext_test_case import TorchtextTestCase
def _get_mock_dataset(root_dir, base_dir_name):
"""
root_dir: directory to the mocked dataset
base_dir_name: YelpReviewPolarity or YelpReviewFull
"""
base_dir = os.path.join(root_dir, base_dir_name)
temp_dataset_dir = os.path.join(base_dir, "temp_dataset_dir")
os.makedirs(temp_dataset_dir, exist_ok=True)
seed = 1
mocked_data = defaultdict(list)
for file_name in ("train.csv", "test.csv"):
csv_file = os.path.join(temp_dataset_dir, file_name)
mocked_lines = mocked_data[os.path.splitext(file_name)[0]]
with open(csv_file, "w", encoding="utf-8") as f:
for i in range(5):
if base_dir_name == YelpReviewPolarity.__name__:
label = seed % 2 + 1
else:
label = seed % 5 + 1
rand_string = get_random_unicode(seed)
dataset_line = (label, f"{rand_string}")
f.write(f'"{label}","{rand_string}"\n')
# append line to correct dataset split
mocked_lines.append(dataset_line)
seed += 1
if base_dir_name == YelpReviewPolarity.__name__:
compressed_file = "yelp_review_polarity_csv"
else:
compressed_file = "yelp_review_full_csv"
compressed_dataset_path = os.path.join(base_dir, compressed_file + ".tar.gz")
# create gz file from dataset folder
with tarfile.open(compressed_dataset_path, "w:gz") as tar:
tar.add(temp_dataset_dir, arcname=compressed_file)
return mocked_data
class TestYelpReviews(TempDirMixin, TorchtextTestCase):
root_dir = None
samples = []
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.root_dir = cls.get_base_temp_dir()
cls.patcher = patch("torchdata.datapipes.iter.util.cacheholder._hash_check", return_value=True)
cls.patcher.start()
@classmethod
def tearDownClass(cls):
cls.patcher.stop()
super().tearDownClass()
@nested_params([YelpReviewPolarity, YelpReviewFull], ["train", "test"])
def test_yelpreviews(self, yelp_dataset, split):
expected_samples = _get_mock_dataset(
os.path.join(self.root_dir, "datasets"), base_dir_name=yelp_dataset.__name__
)[split]
dataset = yelp_dataset(root=self.root_dir, split=split)
samples = list(dataset)
for sample, expected_sample in zip_equal(samples, expected_samples):
self.assertEqual(sample, expected_sample)
@nested_params([YelpReviewPolarity, YelpReviewFull], ["train", "test"])
def test_yelpreviews_split_argument(self, yelp_dataset, split):
# call `_get_mock_dataset` to create mock dataset files
_ = _get_mock_dataset(self.root_dir, yelp_dataset.__name__)
dataset1 = yelp_dataset(root=self.root_dir, split=split)
(dataset2,) = yelp_dataset(root=self.root_dir, split=(split,))
for d1, d2 in zip_equal(dataset1, dataset2):
self.assertEqual(d1, d2)
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