diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 33eb44b21d..d9c32f201c 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -42,6 +42,11 @@ AmazonReviewPolarity .. autofunction:: AmazonReviewPolarity +CoLA +~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: CoLA + DBpedia ~~~~~~~ diff --git a/test/datasets/test_cola.py b/test/datasets/test_cola.py new file mode 100644 index 0000000000..d55eb31fbf --- /dev/null +++ b/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) diff --git a/torchtext/datasets/__init__.py b/torchtext/datasets/__init__.py index d7d33298ad..29dcc5f165 100644 --- a/torchtext/datasets/__init__.py +++ b/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, diff --git a/torchtext/datasets/cola.py b/torchtext/datasets/cola.py new file mode 100644 index 0000000000..d52cb0be66 --- /dev/null +++ b/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()