def test_data_home():
# get_data_home will point to a pre-existing folder
data_home = get_data_home(data_home=DATA_HOME)
assert_equals(data_home, DATA_HOME)
assert_true(os.path.exists(data_home))
# clear_data_home will delete both the content and the folder it-self
clear_data_home(data_home=data_home)
assert_false(os.path.exists(data_home))
# if the folder is missing it will be created again
data_home = get_data_home(data_home=DATA_HOME)
assert_true(os.path.exists(data_home))
def test_data_home():
# get_data_home will point to a pre-existing folder
data_home = get_data_home(data_home=DATA_HOME)
assert_equals(data_home, DATA_HOME)
assert_true(os.path.exists(data_home))
# clear_data_home will delete both the content and the folder it-self
clear_data_home(data_home=data_home)
assert_false(os.path.exists(data_home))
# if the folder is missing it will be created again
data_home = get_data_home(data_home=DATA_HOME)
assert_true(os.path.exists(data_home))
def test_load_lfw_pairs():
if not os.path.exists(os.path.join(get_data_home(), 'lfw_home')):
raise SkipTest
lfw_pairs_train = load_lfw_pairs(subset='train')
# this dataset is used for training supervised face verification models,
# this is a binary classification task
top_classes = ['Different persons', 'Same person']
assert_array_equal(lfw_pairs_train.target_names, top_classes)
# default slice is a rectangular shape around the face, removing
# most of the background, for each of the 2 face pictures
assert_equal(lfw_pairs_train.data.shape, (2200, 2, 62, 47))
# the ordering is respecting the metadata text file of the official LFW
# tasks
assert_equal(lfw_pairs_train.target.shape, (2200, ))
assert_array_equal(lfw_pairs_train.target[:5], [1, 1, 1, 1, 1])
assert_array_equal(lfw_pairs_train.target[-5:], [0, 0, 0, 0, 0])
# as for the people loader it is also possible to load the color channels
# in the last dimension
lfw_pairs_train = load_lfw_pairs(subset='train', color=True)
assert_equal(lfw_pairs_train.data.shape, (2200, 2, 62, 47, 3))
# the data also has a test development set and a 10-fold CV dataset for
# final evaluation
lfw_pairs_test = load_lfw_pairs(subset='test')
assert_equal(lfw_pairs_test.data.shape, (1000, 2, 62, 47))
lfw_pairs_10_folds = load_lfw_pairs(subset='10_folds')
assert_equal(lfw_pairs_10_folds.data.shape, (6000, 2, 62, 47))
def test_load_lfw_pairs():
if not os.path.exists(os.path.join(get_data_home(), 'lfw_home')):
raise SkipTest
lfw_pairs_train = load_lfw_pairs(subset='train')
# this dataset is used for training supervised face verification models,
# this is a binary classification task
top_classes = ['Different persons', 'Same person']
assert_array_equal(lfw_pairs_train.target_names, top_classes)
# default slice is a rectangular shape around the face, removing
# most of the background, for each of the 2 face pictures
assert_equal(lfw_pairs_train.data.shape, (2200, 2, 62, 47))
# the ordering is respecting the metadata text file of the official LFW
# tasks
assert_equal(lfw_pairs_train.target.shape, (2200,))
assert_array_equal(lfw_pairs_train.target[:5], [1, 1, 1, 1, 1])
assert_array_equal(lfw_pairs_train.target[-5:], [0, 0, 0, 0, 0])
# as for the people loader it is also possible to load the color channels
# in the last dimension
lfw_pairs_train = load_lfw_pairs(subset='train', color=True)
assert_equal(lfw_pairs_train.data.shape, (2200, 2, 62, 47, 3))
# the data also has a test development set and a 10-fold CV dataset for
# final evaluation
lfw_pairs_test = load_lfw_pairs(subset='test')
assert_equal(lfw_pairs_test.data.shape, (1000, 2, 62, 47))
lfw_pairs_10_folds = load_lfw_pairs(subset='10_folds')
assert_equal(lfw_pairs_10_folds.data.shape, (6000, 2, 62, 47))
def test_load_lfw_people():
if not os.path.exists(os.path.join(get_data_home(), 'lfw_home')):
# skip this test is the data has not already been previously
# downloaded to avoid having tests rely on the availability of a
# fast internet connection
# to download the data, run the face recognition / verification
# examples or call fetch_lfw_people function from an interactive shell
# for instance
raise SkipTest
lfw_people = load_lfw_people(min_faces_per_person=100)
# only 5 person have more than 100 pictures each in the dataset
top_classes = [
'Colin Powell', 'Donald Rumsfeld', 'George W Bush',
'Gerhard Schroeder', 'Tony Blair'
]
assert_array_equal(lfw_people.target_names, top_classes)
# default slice is a rectangular shape around the face, removing
# most of the background
assert_equal(lfw_people.data.shape, (1140, 62, 47))
# person ids have been shuffled to avoid having the photo ordered by
# alphabetical ordering as in the default tarball layout
assert_equal(lfw_people.target.shape, (1140, ))
assert_array_equal(lfw_people.target[:5], [2, 3, 1, 4, 1])
# it is possible to slice the data in different ways and to resize the
# outpout without changing the width / heigh ratio
lfw_people = load_lfw_people(min_faces_per_person=100,
slice_=(slice(50, 200), slice(50, 200)),
resize=0.1)
assert_equal(lfw_people.data.shape, (1140, 15, 15))
# it is also possible to load the color version of the data, in that
# case the color channels are stored in the last dimension of the data
lfw_people = load_lfw_people(min_faces_per_person=100, color=True)
assert_equal(lfw_people.data.shape, (1140, 62, 47, 3))
def test_load_lfw_people():
if not os.path.exists(os.path.join(get_data_home(), 'lfw_home')):
# skip this test is the data has not already been previously
# downloaded to avoid having tests rely on the availability of a
# fast internet connection
# to download the data, run the face recognition / verification
# examples or call fetch_lfw_people function from an interactive shell
# for instance
raise SkipTest
lfw_people = load_lfw_people(min_faces_per_person=100)
# only 5 person have more than 100 pictures each in the dataset
top_classes = ['Colin Powell', 'Donald Rumsfeld', 'George W Bush',
'Gerhard Schroeder', 'Tony Blair']
assert_array_equal(lfw_people.target_names, top_classes)
# default slice is a rectangular shape around the face, removing
# most of the background
assert_equal(lfw_people.data.shape, (1140, 62, 47))
# person ids have been shuffled to avoid having the photo ordered by
# alphabetical ordering as in the default tarball layout
assert_equal(lfw_people.target.shape, (1140,))
assert_array_equal(lfw_people.target[:5], [2, 3, 1, 4, 1])
# it is possible to slice the data in different ways and to resize the
# outpout without changing the width / heigh ratio
lfw_people = load_lfw_people(min_faces_per_person=100,
slice_=(slice(50, 200), slice(50, 200)),
resize=0.1)
assert_equal(lfw_people.data.shape, (1140, 15, 15))
# it is also possible to load the color version of the data, in that
# case the color channels are stored in the last dimension of the data
lfw_people = load_lfw_people(min_faces_per_person=100, color=True)
assert_equal(lfw_people.data.shape, (1140, 62, 47, 3))
def setup_module(module):
data_home = get_data_home()
if not exists(join(data_home, '20news_home')):
raise SkipTest("Skipping dataset loading doctests")
def setup_module(module):
if not exists(get_data_home()):
raise SkipTest("Skipping dataset loading doctests")
def setup_module(module):
if not exists(get_data_home()):
raise SkipTest("Skipping dataset loading doctests")
