test: added tests

src/safeds_datasets/image/_mnist/_mnist.py

@@ -2,7 +2,6 @@
 import os
 import struct
 import sys
-import tempfile
 import urllib.request
 from array import array
 from pathlib import Path
@@ -160,16 +159,16 @@ def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int,
             with gzip.open(path / file_path, mode='rb') as label_file:
                 magic, size = struct.unpack(">II", label_file.read(8))
                 if magic != 2049:
-                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2049.")
+                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2049.")  # pragma: no cover
                 if "train" in file_name:
                     train_labels = Column(file_name, [labels[label_index] for label_index in array("B", label_file.read())])
                 else:
-                    test_labels = Column(file_name, array("B", label_file.read()))
+                    test_labels = Column(file_name, [labels[label_index] for label_index in array("B", label_file.read())])
         else:
             with gzip.open(path / file_path, mode='rb') as image_file:
                 magic, size, rows, cols = struct.unpack(">IIII", image_file.read(16))
                 if magic != 2051:
-                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2051.")
+                    raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2051.")  # pragma: no cover
                 image_data = array("B", image_file.read())
                 image_tensor = torch.empty(size, 1, rows, cols)
                 for i in range(size):
@@ -183,7 +182,7 @@ def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int,
                 else:
                     test_image_list = image_list
     if train_image_list is None or test_image_list is None or train_labels is None or test_labels is None:
-        raise ValueError
+        raise ValueError  # pragma: no cover
     return ImageDataset[Column](train_image_list, train_labels, 32, shuffle=True), ImageDataset[Column](test_image_list, test_labels, 32)
 
 
@@ -197,7 +196,7 @@ def _download_mnist_like(path: str | Path, files: dict[str, str], links: list[st
                 print()  # noqa: T201
                 break
             except HTTPError as e:
-                print(f"An error occurred while downloading: {e}")  # noqa: T201
+                print(f"An error occurred while downloading: {e}")  # noqa: T201  # pragma: no cover
 
 
 def _report_download_progress(current_packages: int, package_size: int, file_size: int) -> None:

tests/safeds_datasets/image/_mnist/test_mnist.py

@@ -0,0 +1,74 @@
+import os
+import tempfile
+from pathlib import Path
+
+import pytest
+from safeds.data.labeled.containers import ImageDataset
+
+from safeds_datasets.image import load_mnist, _mnist, load_fashion_mnist, load_kmnist
+
+
+class TestMNIST:
+
+    def test_should_download_and_return_mnist(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            train, test = load_mnist(tmpdirname, True)
+            files = os.listdir(Path(tmpdirname) / _mnist._mnist._mnist_folder)
+            for mnist_file in _mnist._mnist._mnist_files.values():
+                assert mnist_file in files
+            assert isinstance(train, ImageDataset)
+            assert isinstance(test, ImageDataset)
+            assert len(train) == 60_000
+            assert len(test) == 10_000
+            train_output = train.get_output()
+            test_output = test.get_output()
+            assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._mnist_labels.values())
+
+    def test_should_raise_if_file_not_found(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with pytest.raises(FileNotFoundError):
+                load_mnist(tmpdirname, False)
+
+
+class TestFashionMNIST:
+
+    def test_should_download_and_return_mnist(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            train, test = load_fashion_mnist(tmpdirname, True)
+            files = os.listdir(Path(tmpdirname) / _mnist._mnist._fashion_mnist_folder)
+            for mnist_file in _mnist._mnist._fashion_mnist_files.values():
+                assert mnist_file in files
+            assert isinstance(train, ImageDataset)
+            assert isinstance(test, ImageDataset)
+            assert len(train) == 60_000
+            assert len(test) == 10_000
+            train_output = train.get_output()
+            test_output = test.get_output()
+            assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._fashion_mnist_labels.values())
+
+    def test_should_raise_if_file_not_found(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with pytest.raises(FileNotFoundError):
+                load_fashion_mnist(tmpdirname, False)
+
+
+class TestKMNIST:
+
+    def test_should_download_and_return_mnist(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            train, test = load_kmnist(tmpdirname, True)
+            files = os.listdir(Path(tmpdirname) / _mnist._mnist._kuzushiji_mnist_folder)
+            for mnist_file in _mnist._mnist._kuzushiji_mnist_files.values():
+                assert mnist_file in files
+            assert isinstance(train, ImageDataset)
+            assert isinstance(test, ImageDataset)
+            assert len(train) == 60_000
+            assert len(test) == 10_000
+            train_output = train.get_output()
+            test_output = test.get_output()
+            assert set(train_output.get_unique_values()) == set(test_output.get_unique_values()) == set(_mnist._mnist._kuzushiji_mnist_labels.values())
+
+    def test_should_raise_if_file_not_found(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with pytest.raises(FileNotFoundError):
+                load_kmnist(tmpdirname, False)