def test_graph_net_and_tvl1_same_for_pure_l1(max_iter=100, decimal=2):
###############################################################
# graph_net_solver and tvl1_solver should give same results
# when l1_ratio = 1.
###############################################################
X, y, _, mask = _make_data()
alpha = .1
unmasked_X = np.rollaxis(X, -1, start=0)
unmasked_X = np.array([x[mask] for x in unmasked_X])
# results should be exactly the same for pure lasso
a = tvl1_solver(unmasked_X, y, alpha, 1., mask, loss="mse",
max_iter=max_iter)[0]
b = _graph_net_squared_loss(unmasked_X, y, alpha, 1.,
max_iter=max_iter,
mask=mask)[0]
mask = nibabel.Nifti1Image(mask.astype(np.float), np.eye(4))
X = nibabel.Nifti1Image(X.astype(np.float), np.eye(4))
for standardize in [True, False]:
sl = BaseSpaceNet(
alphas=alpha, l1_ratios=1., mask=mask, penalty="graph-net",
max_iter=max_iter, standardize=standardize).fit(X, y)
tvl1 = BaseSpaceNet(
alphas=alpha, l1_ratios=1., mask=mask, penalty="tv-l1",
max_iter=max_iter, standardize=standardize).fit(X, y)
# Should be exactly the same (except for numerical errors).
# However because of the TV-L1 prox approx, results might be 'slightly'
# different.
np.testing.assert_array_almost_equal(a, b, decimal=decimal)
np.testing.assert_array_almost_equal(sl.coef_, tvl1.coef_,
decimal=decimal)
def test_graph_net_and_tvl1_same_for_pure_l1_logistic(max_iter=20,
decimal=2):
###############################################################
# graph_net_solver and tvl1_solver should give same results
# when l1_ratio = 1.
###############################################################
iris = load_iris()
X, y = iris.data, iris.target
y = y > 0.
alpha = 1. / X.shape[0]
X_, mask_ = to_niimgs(X, (2, 2, 2))
mask = mask_.get_data().astype(np.bool).ravel()
# results should be exactly the same for pure lasso
a = _graph_net_logistic(X, y, alpha, 1., mask=mask,
max_iter=max_iter)[0]
b = tvl1_solver(X, y, alpha, 1., loss="logistic", mask=mask,
max_iter=max_iter)[0]
for standardize in [True, False]:
sl = SpaceNetClassifier(
alphas=alpha,
l1_ratios=1.,
max_iter=max_iter,
mask=mask_,
penalty="graph-net",
standardize=standardize).fit(
X_,
y)
tvl1 = SpaceNetClassifier(
alphas=alpha,
l1_ratios=1.,
max_iter=max_iter,
mask=mask_,
penalty="tv-l1",
standardize=standardize).fit(
X_,
y)
# should be exactly the same (except for numerical errors)
np.testing.assert_array_almost_equal(a, b, decimal=decimal)
np.testing.assert_array_almost_equal(sl.coef_[0], tvl1.coef_[0],
decimal=decimal)
def test_tvl1_solver_raises_value_error_if_invalid_loss():
pytest.raises(ValueError, lambda loss: tvl1_solver(
np.array([[1]]), None, None, None, None, loss=loss),
"invalidloss")
def test_tvl1_solver_raises_value_error_if_invalid_loss():
assert_raises(ValueError, lambda loss: tvl1_solver(
np.array([[1]]), None, None, None, None, loss=loss),
"invalidloss")