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TST add test to check that all ridge solver give the same results #13914

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Merged
merged 8 commits into from
Jun 11, 2019
Merged

TST add test to check that all ridge solver give the same results #13914

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d33d410
add test to check that all ridge solver give the same results
jeromedockes May 21, 2019
73ad6e1
Merge remote-tracking branch 'upstream/master' into add_test_ridge_so…
jeromedockes May 21, 2019
294db99
p -> proportion_nonzero
jeromedockes May 28, 2019
64bf535
Merge remote-tracking branch 'upstream/master' into add_test_ridge_so…
jeromedockes May 28, 2019
3f308ba
use svd solver as reference
jeromedockes May 28, 2019
af88bcc
reduce number of seeds in ridge test
jeromedockes Jun 10, 2019
0f08cbc
avoid unnecessary copies in test
jeromedockes Jun 11, 2019
762fe5d
avoid skipping tests
jeromedockes Jun 11, 2019
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35 changes: 35 additions & 0 deletions sklearn/linear_model/tests/test_ridge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -393,6 +393,41 @@ def _make_sparse_offset_regression(
return X, y


@pytest.mark.parametrize(
'solver, sparse_X',
((solver, sparse_X) for
(solver, sparse_X) in product(
['cholesky', 'sag', 'sparse_cg', 'lsqr', 'saga', 'ridgecv'],
[False, True])
if not (sparse_X and solver not in ['sparse_cg', 'ridgecv'])))
@pytest.mark.parametrize(
'n_samples,dtype,proportion_nonzero',
[(20, 'float32', .1), (40, 'float32', 1.), (20, 'float64', .2)])
@pytest.mark.parametrize('seed', np.arange(3))
def test_solver_consistency(
solver, proportion_nonzero, n_samples, dtype, sparse_X, seed):
alpha = 1.
noise = 50. if proportion_nonzero > .9 else 500.
X, y = _make_sparse_offset_regression(
bias=10, n_features=30, proportion_nonzero=proportion_nonzero,
noise=noise, random_state=seed, n_samples=n_samples)
svd_ridge = Ridge(
solver='svd', normalize=True, alpha=alpha).fit(X, y)
X = X.astype(dtype, copy=False)
y = y.astype(dtype, copy=False)
if sparse_X:
X = sp.csr_matrix(X)
if solver == 'ridgecv':
ridge = RidgeCV(alphas=[alpha], normalize=True)
else:
ridge = Ridge(solver=solver, tol=1e-10, normalize=True, alpha=alpha)
ridge.fit(X, y)
assert_allclose(
ridge.coef_, svd_ridge.coef_, atol=1e-3, rtol=1e-3)
assert_allclose(
ridge.intercept_, svd_ridge.intercept_, atol=1e-3, rtol=1e-3)


@pytest.mark.parametrize('gcv_mode', ['svd', 'eigen'])
@pytest.mark.parametrize('X_constructor', [np.asarray, sp.csr_matrix])
@pytest.mark.parametrize('X_shape', [(11, 8), (11, 20)])
Expand Down