Uploaded Test files

venv/Lib/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
+import os
+import shutil
+import tempfile
+import numpy as np
+import pytest
+from functools import partial
+from sklearn.externals._pilutil import pillow_installed, imsave
+from sklearn.datasets import fetch_lfw_pairs
+from sklearn.datasets import fetch_lfw_people
+
+from sklearn.utils._testing import assert_array_equal
+from sklearn.utils._testing import SkipTest
+from sklearn.datasets.tests.test_common import check_return_X_y
+
+
+SCIKIT_LEARN_DATA = None
+SCIKIT_LEARN_EMPTY_DATA = None
+LFW_HOME = None
+
+FAKE_NAMES = [
+    'Abdelatif_Smith',
+    'Abhati_Kepler',
+    'Camara_Alvaro',
+    'Chen_Dupont',
+    'John_Lee',
+    'Lin_Bauman',
+    'Onur_Lopez',
+]
+
+
+def setup_module():
+    """Test fixture run once and common to all tests of this module"""
+    if not pillow_installed:
+        raise SkipTest("PIL not installed.")
+
+    global SCIKIT_LEARN_DATA, SCIKIT_LEARN_EMPTY_DATA, LFW_HOME
+
+    SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
+    LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, 'lfw_home')
+
+    SCIKIT_LEARN_EMPTY_DATA = tempfile.mkdtemp(
+        prefix="scikit_learn_empty_test_")
+
+    if not os.path.exists(LFW_HOME):
+        os.makedirs(LFW_HOME)
+
+    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 = os.path.join(LFW_HOME, 'lfw_funneled', name)
+        if not os.path.exists(folder_name):
+            os.makedirs(folder_name)
+
+        n_faces = np_rng.randint(1, 5)
+        counts[name] = n_faces
+        for i in range(n_faces):
+            file_path = os.path.join(folder_name, name + '_%04d.jpg' % i)
+            uniface = np_rng.randint(0, 255, size=(250, 250, 3))
+            try:
+                imsave(file_path, uniface)
+            except ImportError:
+                raise SkipTest("PIL not installed")
+
+    # add some random file pollution to test robustness
+    with open(os.path.join(LFW_HOME, 'lfw_funneled', '.test.swp'), 'wb') as f:
+        f.write(b'Text file to be ignored by the dataset loader.')
+
+    # generate some pairing metadata files using the same format as LFW
+    with open(os.path.join(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 = random_state.choice(np.arange(counts[first_name]))
+            second_index = random_state.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())
+
+    with open(os.path.join(LFW_HOME, 'pairsDevTest.txt'), 'wb') as f:
+        f.write(b"Fake place holder that won't be tested")
+
+    with open(os.path.join(LFW_HOME, 'pairs.txt'), 'wb') as f:
+        f.write(b"Fake place holder that won't be tested")
+
+
+def teardown_module():
+    """Test fixture (clean up) run once after all tests of this module"""
+    if os.path.isdir(SCIKIT_LEARN_DATA):
+        shutil.rmtree(SCIKIT_LEARN_DATA)
+    if os.path.isdir(SCIKIT_LEARN_EMPTY_DATA):
+        shutil.rmtree(SCIKIT_LEARN_EMPTY_DATA)
+
+
+def test_load_empty_lfw_people():
+    with pytest.raises(IOError):
+        fetch_lfw_people(data_home=SCIKIT_LEARN_EMPTY_DATA,
+                         download_if_missing=False)
+
+
+def test_load_fake_lfw_people():
+    lfw_people = fetch_lfw_people(data_home=SCIKIT_LEARN_DATA,
+                                  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=SCIKIT_LEARN_DATA, resize=None,
+                                  slice_=None, color=True,
+                                  download_if_missing=False)
+    assert lfw_people.images.shape == (17, 250, 250, 3)
+
+    # 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=SCIKIT_LEARN_DATA,
+                         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():
+    with pytest.raises(ValueError):
+        fetch_lfw_people(data_home=SCIKIT_LEARN_DATA, min_faces_per_person=100,
+                         download_if_missing=False)
+
+
+def test_load_empty_lfw_pairs():
+    with pytest.raises(IOError):
+        fetch_lfw_pairs(data_home=SCIKIT_LEARN_EMPTY_DATA,
+                        download_if_missing=False)
+
+
+def test_load_fake_lfw_pairs():
+    lfw_pairs_train = fetch_lfw_pairs(data_home=SCIKIT_LEARN_DATA,
+                                      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=SCIKIT_LEARN_DATA, 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)