Add AmazonReviewPolarity Mocked Unit Test

test/common/case_utils.py

@@ -1,7 +1,40 @@
+import os.path
+import tempfile
 import unittest
+
 from torchtext._internal.module_utils import is_module_available
 
 
+class TempDirMixin:
+    """Mixin to provide easy access to temp dir"""
+
+    temp_dir_ = None
+
+    @classmethod
+    def get_base_temp_dir(cls):
+        # If TORCHTEXT_TEST_TEMP_DIR is set, use it instead of temporary directory.
+        # this is handy for debugging.
+        key = "TORCHTEXT_TEST_TEMP_DIR"
+        if key in os.environ:
+            return os.environ[key]
+        if cls.temp_dir_ is None:
+            cls.temp_dir_ = tempfile.TemporaryDirectory()
+        return cls.temp_dir_.name
+
+    @classmethod
+    def tearDownClass(cls):
+        super().tearDownClass()
+        if cls.temp_dir_ is not None:
+            cls.temp_dir_.cleanup()
+            cls.temp_dir_ = None
+
+    def get_temp_path(self, *paths):
+        temp_dir = os.path.join(self.get_base_temp_dir(), self.id())
+        path = os.path.join(temp_dir, *paths)
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        return path
+
+
 def skipIfNoModule(module, display_name=None):
     display_name = display_name or module
     return unittest.skipIf(not is_module_available(module), f'"{display_name}" is not available')

test/datasets/amazonreviewpolarity_test.py

@@ -0,0 +1,82 @@
+import os
+import random
+import string
+import tarfile
+from collections import defaultdict
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.datasets.amazonreviewpolarity import AmazonReviewPolarity
+
+from ..common.case_utils import TempDirMixin
+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, "AmazonReviewPolarity")
+    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"):
+        txt_file = os.path.join(temp_dataset_dir, file_name)
+        with open(txt_file, "w") as f:
+            for i in range(5):
+                label = seed % 2 + 1
+                rand_string = " ".join(
+                    random.choice(string.ascii_letters) for i in range(seed)
+                )
+                dataset_line = (label, f"{rand_string} {rand_string}")
+                # append line to correct dataset split
+                mocked_data[os.path.splitext(file_name)[0]].append(dataset_line)
+                f.write(f'"{label}","{rand_string}","{rand_string}"\n')
+                seed += 1
+
+    compressed_dataset_path = os.path.join(
+        base_dir, "amazon_review_polarity_csv.tar.gz"
+    )
+    # create tar file from dataset folder
+    with tarfile.open(compressed_dataset_path, "w:gz") as tar:
+        tar.add(temp_dataset_dir, arcname="amazon_review_polarity_csv")
+
+    return mocked_data
+
+
+class TestAmazonReviewPolarity(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)
+
+    @parameterized.expand(["train", "test"])
+    def test_amazon_review_polarity(self, split):
+        with patch(
+            "torchdata.datapipes.iter.util.cacheholder._hash_check", return_value=True
+        ):
+            dataset = AmazonReviewPolarity(root=self.root_dir, split=split)
+            n_iter = 0
+            for i, (label, text) in enumerate(dataset):
+                expected_sample = self.samples[split][i]
+                assert label == expected_sample[0]
+                assert text == expected_sample[1]
+                n_iter += 1
+            assert n_iter == len(self.samples[split])
+
+    @parameterized.expand([("train", ("train",)), ("test", ("test",))])
+    def test_amazon_review_polarity_split_argument(self, split1, split2):
+        with patch(
+            "torchdata.datapipes.iter.util.cacheholder._hash_check", return_value=True
+        ):
+            dataset1 = AmazonReviewPolarity(root=self.root_dir, split=split1)
+            (dataset2,) = AmazonReviewPolarity(root=self.root_dir, split=split2)
+
+            for d1, d2 in zip(dataset1, dataset2):
+                self.assertEqual(d1, d2)