import pytest
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.metrics.pairwise import rbf_kernel
from sklearn_extra.kernel_approximation import Fastfood
# generate data
rng = np.random.RandomState(0)
X = rng.random_sample(size=(300, 50))
Y = rng.random_sample(size=(300, 50))
X /= X.sum(axis=1)[:, np.newaxis]
Y /= Y.sum(axis=1)[:, np.newaxis]
@pytest.mark.parametrize(
"message, input_, expected",
[
("test n is scaled to be a multiple of d", (16, 20), (16, 32, 2)),
("test n equals d", (16, 16), (16, 16, 1)),
("test n becomes power of two", (3, 16), (4, 16, 4)),
("test all", (7, 12), (8, 16, 2)),
],
)
def test_fastfood_enforce_dimensionality_constraint(message, input_, expected):
d, n = input_
output = Fastfood._enforce_dimensionality_constraints(d, n)
assert expected == output, message
def test_fastfood():
"""test that Fastfood fast approximates kernel on random data"""
# compute exact kernel
gamma = 10.0
kernel = rbf_kernel(X, Y, gamma=gamma)
sigma = np.sqrt(1 / (2 * gamma))
# approximate kernel mapping
ff_transform = Fastfood(sigma, n_components=1000, random_state=42)
pars = ff_transform.fit(X)
X_trans = pars.transform(X)
Y_trans = ff_transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
print("approximation:", kernel_approx[:5, :5])
print("true kernel:", kernel[:5, :5])
assert_array_almost_equal(kernel, kernel_approx, decimal=1)
