some new features

.venv/lib/python3.12/site-packages/sklearn/datasets/tests/test_lfw.py

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+"""This test for the LFW require medium-size data downloading and processing
+
+If the data has not been already downloaded by running the examples,
+the tests won't run (skipped).
+
+If the test are run, the first execution will be long (typically a bit
+more than a couple of minutes) but as the dataset loader is leveraging
+joblib, successive runs will be fast (less than 200ms).
+"""
+
+import random
+from functools import partial
+
+import numpy as np
+import pytest
+
+from sklearn.datasets import fetch_lfw_pairs, fetch_lfw_people
+from sklearn.datasets.tests.test_common import check_return_X_y
+from sklearn.utils._testing import assert_array_equal
+
+FAKE_NAMES = [
+    "Abdelatif_Smith",
+    "Abhati_Kepler",
+    "Camara_Alvaro",
+    "Chen_Dupont",
+    "John_Lee",
+    "Lin_Bauman",
+    "Onur_Lopez",
+]
+
+
+@pytest.fixture(scope="module")
+def mock_empty_data_home(tmp_path_factory):
+    data_dir = tmp_path_factory.mktemp("scikit_learn_empty_test")
+
+    yield data_dir
+
+
+@pytest.fixture(scope="module")
+def mock_data_home(tmp_path_factory):
+    """Test fixture run once and common to all tests of this module"""
+    Image = pytest.importorskip("PIL.Image")
+
+    data_dir = tmp_path_factory.mktemp("scikit_learn_lfw_test")
+    lfw_home = data_dir / "lfw_home"
+    lfw_home.mkdir(parents=True, exist_ok=True)
+
+    random_state = random.Random(42)
+    np_rng = np.random.RandomState(42)
+
+    # generate some random jpeg files for each person
+    counts = {}
+    for name in FAKE_NAMES:
+        folder_name = lfw_home / "lfw_funneled" / name
+        folder_name.mkdir(parents=True, exist_ok=True)
+
+        n_faces = np_rng.randint(1, 5)
+        counts[name] = n_faces
+        for i in range(n_faces):
+            file_path = folder_name / (name + "_%04d.jpg" % i)
+            uniface = np_rng.randint(0, 255, size=(250, 250, 3))
+            img = Image.fromarray(uniface.astype(np.uint8))
+            img.save(file_path)
+
+    # add some random file pollution to test robustness
+    (lfw_home / "lfw_funneled" / ".test.swp").write_bytes(
+        b"Text file to be ignored by the dataset loader."
+    )
+
+    # generate some pairing metadata files using the same format as LFW
+    with open(lfw_home / "pairsDevTrain.txt", "wb") as f:
+        f.write(b"10\n")
+        more_than_two = [name for name, count in counts.items() if count >= 2]
+        for i in range(5):
+            name = random_state.choice(more_than_two)
+            first, second = random_state.sample(range(counts[name]), 2)
+            f.write(("%s\t%d\t%d\n" % (name, first, second)).encode())
+
+        for i in range(5):
+            first_name, second_name = random_state.sample(FAKE_NAMES, 2)
+            first_index = np_rng.choice(np.arange(counts[first_name]))
+            second_index = np_rng.choice(np.arange(counts[second_name]))
+            f.write(
+                (
+                    "%s\t%d\t%s\t%d\n"
+                    % (first_name, first_index, second_name, second_index)
+                ).encode()
+            )
+
+    (lfw_home / "pairsDevTest.txt").write_bytes(
+        b"Fake place holder that won't be tested"
+    )
+    (lfw_home / "pairs.txt").write_bytes(b"Fake place holder that won't be tested")
+
+    yield data_dir
+
+
+def test_load_empty_lfw_people(mock_empty_data_home):
+    with pytest.raises(OSError):
+        fetch_lfw_people(data_home=mock_empty_data_home, download_if_missing=False)
+
+
+def test_load_fake_lfw_people(mock_data_home):
+    lfw_people = fetch_lfw_people(
+        data_home=mock_data_home, min_faces_per_person=3, download_if_missing=False
+    )
+
+    # The data is croped around the center as a rectangular bounding box
+    # around the face. Colors are converted to gray levels:
+    assert lfw_people.images.shape == (10, 62, 47)
+    assert lfw_people.data.shape == (10, 2914)
+
+    # the target is array of person integer ids
+    assert_array_equal(lfw_people.target, [2, 0, 1, 0, 2, 0, 2, 1, 1, 2])
+
+    # names of the persons can be found using the target_names array
+    expected_classes = ["Abdelatif Smith", "Abhati Kepler", "Onur Lopez"]
+    assert_array_equal(lfw_people.target_names, expected_classes)
+
+    # It is possible to ask for the original data without any croping or color
+    # conversion and not limit on the number of picture per person
+    lfw_people = fetch_lfw_people(
+        data_home=mock_data_home,
+        resize=None,
+        slice_=None,
+        color=True,
+        download_if_missing=False,
+    )
+    assert lfw_people.images.shape == (17, 250, 250, 3)
+    assert lfw_people.DESCR.startswith(".. _labeled_faces_in_the_wild_dataset:")
+
+    # the ids and class names are the same as previously
+    assert_array_equal(
+        lfw_people.target, [0, 0, 1, 6, 5, 6, 3, 6, 0, 3, 6, 1, 2, 4, 5, 1, 2]
+    )
+    assert_array_equal(
+        lfw_people.target_names,
+        [
+            "Abdelatif Smith",
+            "Abhati Kepler",
+            "Camara Alvaro",
+            "Chen Dupont",
+            "John Lee",
+            "Lin Bauman",
+            "Onur Lopez",
+        ],
+    )
+
+    # test return_X_y option
+    fetch_func = partial(
+        fetch_lfw_people,
+        data_home=mock_data_home,
+        resize=None,
+        slice_=None,
+        color=True,
+        download_if_missing=False,
+    )
+    check_return_X_y(lfw_people, fetch_func)
+
+
+def test_load_fake_lfw_people_too_restrictive(mock_data_home):
+    with pytest.raises(ValueError):
+        fetch_lfw_people(
+            data_home=mock_data_home,
+            min_faces_per_person=100,
+            download_if_missing=False,
+        )
+
+
+def test_load_empty_lfw_pairs(mock_empty_data_home):
+    with pytest.raises(OSError):
+        fetch_lfw_pairs(data_home=mock_empty_data_home, download_if_missing=False)
+
+
+def test_load_fake_lfw_pairs(mock_data_home):
+    lfw_pairs_train = fetch_lfw_pairs(
+        data_home=mock_data_home, download_if_missing=False
+    )
+
+    # The data is croped around the center as a rectangular bounding box
+    # around the face. Colors are converted to gray levels:
+    assert lfw_pairs_train.pairs.shape == (10, 2, 62, 47)
+
+    # the target is whether the person is the same or not
+    assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
+
+    # names of the persons can be found using the target_names array
+    expected_classes = ["Different persons", "Same person"]
+    assert_array_equal(lfw_pairs_train.target_names, expected_classes)
+
+    # It is possible to ask for the original data without any croping or color
+    # conversion
+    lfw_pairs_train = fetch_lfw_pairs(
+        data_home=mock_data_home,
+        resize=None,
+        slice_=None,
+        color=True,
+        download_if_missing=False,
+    )
+    assert lfw_pairs_train.pairs.shape == (10, 2, 250, 250, 3)
+
+    # the ids and class names are the same as previously
+    assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
+    assert_array_equal(lfw_pairs_train.target_names, expected_classes)
+
+    assert lfw_pairs_train.DESCR.startswith(".. _labeled_faces_in_the_wild_dataset:")
+
+
+def test_fetch_lfw_people_internal_cropping(mock_data_home):
+    """Check that we properly crop the images.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/24942
+    """
+    # If cropping was not done properly and we don't resize the images, the images would
+    # have their original size (250x250) and the image would not fit in the NumPy array
+    # pre-allocated based on `slice_` parameter.
+    slice_ = (slice(70, 195), slice(78, 172))
+    lfw = fetch_lfw_people(
+        data_home=mock_data_home,
+        min_faces_per_person=3,
+        download_if_missing=False,
+        resize=None,
+        slice_=slice_,
+    )
+    assert lfw.images[0].shape == (
+        slice_[0].stop - slice_[0].start,
+        slice_[1].stop - slice_[1].start,
+    )