def test_polynomial_count_sketch_dense_sparse(gamma, degree, coef0):
"""Check that PolynomialCountSketch results are the same for dense and sparse
input.
"""
ps_dense = PolynomialCountSketch(n_components=500,
gamma=gamma,
degree=degree,
coef0=coef0,
random_state=42)
Xt_dense = ps_dense.fit_transform(X)
Yt_dense = ps_dense.transform(Y)
ps_sparse = PolynomialCountSketch(n_components=500,
gamma=gamma,
degree=degree,
coef0=coef0,
random_state=42)
Xt_sparse = ps_sparse.fit_transform(csr_matrix(X))
Yt_sparse = ps_sparse.transform(csr_matrix(Y))
assert_allclose(Xt_dense, Xt_sparse)
assert_allclose(Yt_dense, Yt_sparse)
def test_polynomial_count_sketch(X, Y, gamma, degree, coef0):
# test that PolynomialCountSketch approximates polynomial
# kernel on random data
# compute exact kernel
kernel = polynomial_kernel(X, Y, gamma=gamma, degree=degree, coef0=coef0)
# approximate kernel mapping
ps_transform = PolynomialCountSketch(n_components=5000, gamma=gamma,
coef0=coef0, degree=degree,
random_state=42)
X_trans = ps_transform.fit_transform(X)
Y_trans = ps_transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
error = kernel - kernel_approx
assert np.abs(np.mean(error)) <= 0.05 # close to unbiased
np.abs(error, out=error)
assert np.max(error) <= 0.1 # nothing too far off
assert np.mean(error) <= 0.05 # mean is fairly close