188 lines
7.1 KiB
Python
188 lines
7.1 KiB
Python
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"""This test for the LFW require medium-size data downloading and processing
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If the data has not been already downloaded by running the examples,
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the tests won't run (skipped).
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If the test are run, the first execution will be long (typically a bit
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more than a couple of minutes) but as the dataset loader is leveraging
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joblib, successive runs will be fast (less than 200ms).
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"""
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import random
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import os
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import shutil
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import tempfile
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import numpy as np
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from sklearn.externals import six
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try:
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try:
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from scipy.misc import imsave
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except ImportError:
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from scipy.misc.pilutil import imsave
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except ImportError:
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imsave = None
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from sklearn.datasets import fetch_lfw_pairs
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from sklearn.datasets import fetch_lfw_people
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import SkipTest
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from sklearn.utils.testing import raises
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SCIKIT_LEARN_DATA = tempfile.mkdtemp(prefix="scikit_learn_lfw_test_")
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SCIKIT_LEARN_EMPTY_DATA = tempfile.mkdtemp(prefix="scikit_learn_empty_test_")
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LFW_HOME = os.path.join(SCIKIT_LEARN_DATA, 'lfw_home')
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FAKE_NAMES = [
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'Abdelatif_Smith',
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'Abhati_Kepler',
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'Camara_Alvaro',
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'Chen_Dupont',
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'John_Lee',
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'Lin_Bauman',
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'Onur_Lopez',
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]
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def setup_module():
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"""Test fixture run once and common to all tests of this module"""
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if imsave is None:
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raise SkipTest("PIL not installed.")
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if not os.path.exists(LFW_HOME):
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os.makedirs(LFW_HOME)
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random_state = random.Random(42)
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np_rng = np.random.RandomState(42)
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# generate some random jpeg files for each person
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counts = {}
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for name in FAKE_NAMES:
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folder_name = os.path.join(LFW_HOME, 'lfw_funneled', name)
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if not os.path.exists(folder_name):
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os.makedirs(folder_name)
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n_faces = np_rng.randint(1, 5)
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counts[name] = n_faces
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for i in range(n_faces):
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file_path = os.path.join(folder_name, name + '_%04d.jpg' % i)
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uniface = np_rng.randint(0, 255, size=(250, 250, 3))
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try:
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imsave(file_path, uniface)
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except ImportError:
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raise SkipTest("PIL not installed")
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# add some random file pollution to test robustness
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with open(os.path.join(LFW_HOME, 'lfw_funneled', '.test.swp'), 'wb') as f:
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f.write(six.b('Text file to be ignored by the dataset loader.'))
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# generate some pairing metadata files using the same format as LFW
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with open(os.path.join(LFW_HOME, 'pairsDevTrain.txt'), 'wb') as f:
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f.write(six.b("10\n"))
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more_than_two = [name for name, count in six.iteritems(counts)
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if count >= 2]
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for i in range(5):
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name = random_state.choice(more_than_two)
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first, second = random_state.sample(range(counts[name]), 2)
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f.write(six.b('%s\t%d\t%d\n' % (name, first, second)))
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for i in range(5):
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first_name, second_name = random_state.sample(FAKE_NAMES, 2)
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first_index = random_state.choice(np.arange(counts[first_name]))
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second_index = random_state.choice(np.arange(counts[second_name]))
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f.write(six.b('%s\t%d\t%s\t%d\n' % (first_name, first_index,
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second_name, second_index)))
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with open(os.path.join(LFW_HOME, 'pairsDevTest.txt'), 'wb') as f:
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f.write(six.b("Fake place holder that won't be tested"))
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with open(os.path.join(LFW_HOME, 'pairs.txt'), 'wb') as f:
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f.write(six.b("Fake place holder that won't be tested"))
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def teardown_module():
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"""Test fixture (clean up) run once after all tests of this module"""
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if os.path.isdir(SCIKIT_LEARN_DATA):
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shutil.rmtree(SCIKIT_LEARN_DATA)
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if os.path.isdir(SCIKIT_LEARN_EMPTY_DATA):
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shutil.rmtree(SCIKIT_LEARN_EMPTY_DATA)
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@raises(IOError)
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def test_load_empty_lfw_people():
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fetch_lfw_people(data_home=SCIKIT_LEARN_EMPTY_DATA,
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download_if_missing=False)
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def test_load_fake_lfw_people():
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lfw_people = fetch_lfw_people(data_home=SCIKIT_LEARN_DATA,
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min_faces_per_person=3,
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download_if_missing=False)
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# The data is croped around the center as a rectangular bounding box
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# around the face. Colors are converted to gray levels:
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assert_equal(lfw_people.images.shape, (10, 62, 47))
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assert_equal(lfw_people.data.shape, (10, 2914))
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# the target is array of person integer ids
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assert_array_equal(lfw_people.target, [2, 0, 1, 0, 2, 0, 2, 1, 1, 2])
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# names of the persons can be found using the target_names array
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expected_classes = ['Abdelatif Smith', 'Abhati Kepler', 'Onur Lopez']
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assert_array_equal(lfw_people.target_names, expected_classes)
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# It is possible to ask for the original data without any croping or color
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# conversion and not limit on the number of picture per person
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lfw_people = fetch_lfw_people(data_home=SCIKIT_LEARN_DATA, resize=None,
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slice_=None, color=True,
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download_if_missing=False)
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assert_equal(lfw_people.images.shape, (17, 250, 250, 3))
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# the ids and class names are the same as previously
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assert_array_equal(lfw_people.target,
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[0, 0, 1, 6, 5, 6, 3, 6, 0, 3, 6, 1, 2, 4, 5, 1, 2])
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assert_array_equal(lfw_people.target_names,
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['Abdelatif Smith', 'Abhati Kepler', 'Camara Alvaro',
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'Chen Dupont', 'John Lee', 'Lin Bauman', 'Onur Lopez'])
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@raises(ValueError)
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def test_load_fake_lfw_people_too_restrictive():
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fetch_lfw_people(data_home=SCIKIT_LEARN_DATA, min_faces_per_person=100,
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download_if_missing=False)
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@raises(IOError)
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def test_load_empty_lfw_pairs():
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fetch_lfw_pairs(data_home=SCIKIT_LEARN_EMPTY_DATA,
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download_if_missing=False)
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def test_load_fake_lfw_pairs():
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lfw_pairs_train = fetch_lfw_pairs(data_home=SCIKIT_LEARN_DATA,
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download_if_missing=False)
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# The data is croped around the center as a rectangular bounding box
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# around the face. Colors are converted to gray levels:
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assert_equal(lfw_pairs_train.pairs.shape, (10, 2, 62, 47))
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# the target is whether the person is the same or not
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assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
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# names of the persons can be found using the target_names array
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expected_classes = ['Different persons', 'Same person']
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assert_array_equal(lfw_pairs_train.target_names, expected_classes)
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# It is possible to ask for the original data without any croping or color
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# conversion
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lfw_pairs_train = fetch_lfw_pairs(data_home=SCIKIT_LEARN_DATA, resize=None,
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slice_=None, color=True,
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download_if_missing=False)
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assert_equal(lfw_pairs_train.pairs.shape, (10, 2, 250, 250, 3))
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# the ids and class names are the same as previously
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assert_array_equal(lfw_pairs_train.target, [1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
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assert_array_equal(lfw_pairs_train.target_names, expected_classes)
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