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"""Test kddcup99 loader. Only 'percent10' mode is tested, as the full data
is too big to use in unit-testing.
The test is skipped if the data wasn't previously fetched and saved to
scikit-learn data folder.
"""
from sklearn.datasets import fetch_kddcup99
from sklearn.datasets.tests.test_common import check_return_X_y
from sklearn.utils.testing import assert_equal, SkipTest
from functools import partial
def test_percent10():
try:
data = fetch_kddcup99(download_if_missing=False)
except IOError:
raise SkipTest("kddcup99 dataset can not be loaded.")
assert_equal(data.data.shape, (494021, 41))
assert_equal(data.target.shape, (494021,))
data_shuffled = fetch_kddcup99(shuffle=True, random_state=0)
assert_equal(data.data.shape, data_shuffled.data.shape)
assert_equal(data.target.shape, data_shuffled.target.shape)
data = fetch_kddcup99('SA')
assert_equal(data.data.shape, (100655, 41))
assert_equal(data.target.shape, (100655,))
data = fetch_kddcup99('SF')
assert_equal(data.data.shape, (73237, 4))
assert_equal(data.target.shape, (73237,))
data = fetch_kddcup99('http')
assert_equal(data.data.shape, (58725, 3))
assert_equal(data.target.shape, (58725,))
data = fetch_kddcup99('smtp')
assert_equal(data.data.shape, (9571, 3))
assert_equal(data.target.shape, (9571,))
fetch_func = partial(fetch_kddcup99, 'smtp')
check_return_X_y(data, fetch_func)
def test_shuffle():
try:
dataset = fetch_kddcup99(random_state=0, subset='SA', shuffle=True,
percent10=True, download_if_missing=False)
except IOError:
raise SkipTest("kddcup99 dataset can not be loaded.")
assert(any(dataset.target[-100:] == b'normal.'))
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