from __future__ import division
import os
import sys
import numpy as np
from sklearn.externals.six.moves import range
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils.testing import assert_almost_equal
from nose.tools import assert_raises
if __name__ == '__main__':
# make this copy of skl_groups importable
_this_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, os.path.dirname(os.path.dirname(_this_dir)))
from skl_groups.kernels import MeanMapKernel
################################################################################
def test_mmk():
bags = [np.random.normal(size=(np.random.randint(10, 100), 10))
for _ in range(20)]
res = MeanMapKernel(gamma=2.38).fit_transform(bags)
for i in range(20):
for j in range(20):
exp = pairwise_kernels(bags[j], bags[i], metric='rbf', gamma=2.38)
assert_almost_equal(res[i, j], exp.mean(),
err_msg="({} to {})".format(i, j))
res = MeanMapKernel(kernel='linear').fit(bags[:5]).transform(bags[-2:])
for i in range(5):
for j in range(18, 20):
exp = pairwise_kernels(bags[j], bags[i], metric='linear')
assert_almost_equal(res[j - 18, i], exp.mean(),
err_msg="({} to {})".format(i, j))
# fails on wrong dimension
assert_raises(
ValueError,
lambda:MeanMapKernel().fit(bags).transform([np.random.randn(20, 8)]))
################################################################################
if __name__ == '__main__':
import nose
nose.main()
