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import os
import tarfile
from collections import defaultdict
from unittest.mock import patch
from parameterized import parameterized
from torchtext.datasets.dbpedia import DBpedia
from ..common.case_utils import TempDirMixin, zip_equal, get_random_unicode
from ..common.torchtext_test_case import TorchtextTestCase
def _get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
base_dir = os.path.join(root_dir, "DBpedia")
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):
label = seed % 14 + 1
rand_string = get_random_unicode(seed)
dataset_line = (label, rand_string + " " + rand_string)
f.write(f'{label},"{rand_string}","{rand_string}"\n')
# append line to correct dataset split
mocked_lines.append(dataset_line)
seed += 1
compressed_dataset_path = os.path.join(base_dir, "dbpedia_csv.tar.gz")
# create gz file from dataset folder
with tarfile.open(compressed_dataset_path, "w:gz") as tar:
tar.add(temp_dataset_dir, arcname="dbpedia_csv")
return mocked_data
class TestDBpedia(TempDirMixin, TorchtextTestCase):
root_dir = None
samples = []
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.root_dir = cls.get_base_temp_dir()
cls.samples = _get_mock_dataset(os.path.join(cls.root_dir, "datasets"))
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()
@parameterized.expand(["train", "test"])
def test_dbpedia(self, split):
dataset = DBpedia(root=self.root_dir, split=split)
samples = list(dataset)
expected_samples = self.samples[split]
for sample, expected_sample in zip_equal(samples, expected_samples):
self.assertEqual(sample, expected_sample)
@parameterized.expand(["train", "test"])
def test_dbpedia_split_argument(self, split):
dataset1 = DBpedia(root=self.root_dir, split=split)
(dataset2,) = DBpedia(root=self.root_dir, split=(split,))
for d1, d2 in zip_equal(dataset1, dataset2):
self.assertEqual(d1, d2)
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