Add support for CoLA dataset with unit tests

docs/source/datasets.rst

@@ -42,6 +42,11 @@ AmazonReviewPolarity
 
 .. autofunction:: AmazonReviewPolarity
 
+CoLA
+~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: CoLA
+
 DBpedia
 ~~~~~~~
 

test/datasets/test_cola.py

@@ -0,0 +1,78 @@
+import os
+import zipfile
+from collections import defaultdict
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.datasets.cola import CoLA
+
+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, "CoLA")
+    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 ("in_domain_train.tsv", "in_domain_dev.tsv", "out_of_domain_dev.tsv"):
+        txt_file = os.path.join(temp_dataset_dir, file_name)
+        with open(txt_file, "w", encoding="utf-8") as f:
+            for _ in range(5):
+                label = seed % 2
+                rand_string_1 = get_random_unicode(seed)
+                rand_string_2 = get_random_unicode(seed + 1)
+                dataset_line = (rand_string_1, label, rand_string_2)
+                # append line to correct dataset split
+                mocked_data[os.path.splitext(file_name)[0]].append(dataset_line)
+                f.write(f'"{rand_string_1}"\t"{label}"\t"{rand_string_2}"\n')
+                seed += 1
+
+    compressed_dataset_path = os.path.join(base_dir, "cola_public_1.1.zip")
+    # create zip file from dataset folder
+    with zipfile.ZipFile(compressed_dataset_path, "w") as zip_file:
+        for file_name in ("in_domain_train.tsv", "in_domain_dev.tsv", "out_of_domain_dev.tsv"):
+            txt_file = os.path.join(temp_dataset_dir, file_name)
+            zip_file.write(txt_file, arcname=os.path.join("cola_public", "raw", file_name))
+
+    return mocked_data
+
+
+class TestCoLA(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(cls.root_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()
+
+    @parameterized.expand(["train", "test", "dev"])
+    def test_cola(self, split):
+        dataset = CoLA(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", "dev"])
+    def test_cola_split_argument(self, split):
+        dataset1 = CoLA(root=self.root_dir, split=split)
+        (dataset2,) = CoLA(root=self.root_dir, split=(split,))
+
+        for d1, d2 in zip_equal(dataset1, dataset2):
+            self.assertEqual(d1, d2)

torchtext/datasets/__init__.py

@@ -4,6 +4,7 @@
 from .amazonreviewfull import AmazonReviewFull
 from .amazonreviewpolarity import AmazonReviewPolarity
 from .cc100 import CC100
+from .cola import CoLA
 from .conll2000chunking import CoNLL2000Chunking
 from .dbpedia import DBpedia
 from .enwik9 import EnWik9
@@ -28,6 +29,7 @@
     "AmazonReviewFull": AmazonReviewFull,
     "AmazonReviewPolarity": AmazonReviewPolarity,
     "CC100": CC100,
+    "CoLA": CoLA,
     "CoNLL2000Chunking": CoNLL2000Chunking,
     "DBpedia": DBpedia,
     "EnWik9": EnWik9,

torchtext/datasets/cola.py

@@ -0,0 +1,86 @@
+import csv
+import os
+from typing import Union, Tuple
+
+from torchtext._internal.module_utils import is_module_available
+from torchtext.data.datasets_utils import _create_dataset_directory, _wrap_split_argument
+
+if is_module_available("torchdata"):
+    from torchdata.datapipes.iter import FileOpener, IterableWrapper
+    from torchtext._download_hooks import HttpReader
+
+URL = "https://nyu-mll.github.io/CoLA/cola_public_1.1.zip"
+
+MD5 = "9f6d88c3558ec424cd9d66ea03589aba"
+
+_PATH = "cola_public_1.1.zip"
+
+NUM_LINES = {"train": 8551, "dev": 527, "test": 516}
+
+_EXTRACTED_FILES = {
+    "train": os.path.join("cola_public", "raw", "in_domain_train.tsv"),
+    "dev": os.path.join("cola_public", "raw", "in_domain_dev.tsv"),
+    "test": os.path.join("cola_public", "raw", "out_of_domain_dev.tsv"),
+}
+
+DATASET_NAME = "CoLA"
+
+
+@_create_dataset_directory(dataset_name=DATASET_NAME)
+@_wrap_split_argument(("train", "dev", "test"))
+def CoLA(root: str, split: Union[Tuple[str], str]):
+    """CoLA dataset
+
+    For additional details refer to https://nyu-mll.github.io/CoLA/
+
+    Number of lines per split:
+        - train: 8551
+        - dev: 527
+        - test: 516
+
+    Args:
+        root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')
+        split: split or splits to be returned. Can be a string or tuple of strings. Default: (`train`, `dev`, `test`)
+
+
+    :returns: DataPipe that yields rows from CoLA dataset (source (str), label (int), sentence (str))
+    :rtype: (str, int, str)
+    """
+    if not is_module_available("torchdata"):
+        raise ModuleNotFoundError(
+            "Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`"
+        )
+
+    def _filepath_fn(_=None):
+        return os.path.join(root, _PATH)
+
+    def _extracted_filepath_fn(_=None):
+        return os.path.join(root, _EXTRACTED_FILES[split])
+
+    def _filter_fn(x):
+        return _EXTRACTED_FILES[split] in x[0]
+
+    def _modify_res(t):
+        return (t[0], int(t[1]), t[3])
+
+    def _filter_res(x):
+        return len(x) == 4
+
+    url_dp = IterableWrapper([URL])
+    cache_compressed_dp = url_dp.on_disk_cache(
+        filepath_fn=_filepath_fn,
+        hash_dict={_filepath_fn(): MD5},
+        hash_type="md5",
+    )
+    cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
+
+    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=_extracted_filepath_fn)
+    cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").load_from_zip().filter(_filter_fn)
+    cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
+
+    data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8")
+    # some context stored at top of the file needs to be removed
+    parsed_data = (
+        data_dp.parse_csv(skip_lines=1, delimiter="\t", quoting=csv.QUOTE_NONE).filter(_filter_res).map(_modify_res)
+    )
+    return parsed_data.shuffle().set_shuffle(False).sharding_filter()