def test_anova_kernel(dist, degree, kernel):
# compute exact kernel
gram = anova(X, Y, degree)
# approximate kernel mapping
rk_transform = RandomKernel(n_components=1000, random_state=0,
kernel=kernel, degree=degree,
distribution=dist, p_sparse=0.5)
X_trans = rk_transform.fit_transform(X)
Y_trans = rk_transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
error = gram - kernel_approx
assert np.abs(np.mean(error)) < 0.0001
assert np.max(error) < 0.001 # nothing too far off
assert np.mean(error) < 0.0005 # mean is fairly close
# sparse input
X_trans_sp = rk_transform.transform(X_sp)
assert_allclose_dense_sparse(X_trans, X_trans_sp)
# sparse output
if dist == "sparse_rademacher":
rk_transform.dense_output = False
X_trans_sp = rk_transform.transform(X_sp)
assert issparse(X_trans_sp)
assert_allclose_dense_sparse(X_trans, X_trans_sp.toarray())
else:
rk_transform.dense_output = False
X_trans_sp = rk_transform.transform(X_sp)
assert not issparse(X_trans_sp)
assert_allclose_dense_sparse(X_trans, X_trans_sp)
def test_all_subsets_kernel(dist):
# compute exact kernel
p_sparse = 0.5
kernel = all_subsets(X, Y)
# approximate kernel mapping
rk_transform = RandomKernel(n_components=5000, random_state=0,
kernel='all_subsets',
distribution=dist, p_sparse=p_sparse)
X_trans = rk_transform.fit_transform(X)
Y_trans = rk_transform.transform(Y)
kernel_approx = np.dot(X_trans, Y_trans.T)
error = kernel - kernel_approx
if dist == 'sparse_rademacher':
nnz = rk_transform.random_weights_.nnz
nnz_expect = np.prod(rk_transform.random_weights_.shape)*p_sparse
nnz_var = np.sqrt(nnz_expect * (1-p_sparse))
assert np.abs(nnz-nnz_expect) < 3*nnz_var
assert np.abs(np.mean(error)) < 0.01
assert np.max(error) < 0.1 # nothing too far off
assert np.mean(error) < 0.05 # mean is fairly close
X_trans_sp = rk_transform.transform(X_sp)
assert_allclose_dense_sparse(X_trans, X_trans_sp)
