def test_multitasklasso(gaussian_data, fit_intercept, normalize, alpha):
X, y = gaussian_data
X = [X[0], X[0]]
n_samples = y.shape[1]
Xty = np.array([xx.T.dot(yy) for xx, yy in zip(X, y)])
alpha_max = np.linalg.norm(Xty, axis=0).max()
alpha *= alpha_max / n_samples
est = GroupLasso(alpha=alpha,
fit_intercept=fit_intercept,
normalize=normalize)
est.fit(X, y)
assert hasattr(est, 'is_fitted_')
mtlasso = MultiTaskLasso(alpha=alpha,
fit_intercept=fit_intercept,
normalize=normalize)
mtlasso.fit(X[0], y.T)
assert_allclose(est.coef_, mtlasso.coef_.T, rtol=1e-2)
def test_grouplasso(gaussian_data, fit_intercept, normalize, alpha, positive):
X, y = gaussian_data
n_samples = y.shape[1]
Xty = np.array([xx.T.dot(yy) for xx, yy in zip(X, y)])
alpha_max = np.linalg.norm(Xty, axis=0).max()
alpha *= alpha_max / n_samples
est = GroupLasso(alpha=alpha,
fit_intercept=fit_intercept,
normalize=normalize,
positive=positive)
est.fit(X, y)
assert hasattr(est, 'is_fitted_')
assert_allclose(est.coef_specific_, 0.)
if positive:
assert est.coef_.min() >= 0.
var_y = np.var(y, axis=1)
r2 = 1 - (est.residuals_**2).mean(1) / var_y
scores = est.score(X, y)
assert_allclose(r2, scores)
def test_zero_alpha(gaussian_data):
X, y = gaussian_data
est0 = IndRewLasso(alpha=len(X) * [0.])
est1 = IndLasso(alpha=len(X) * [0.])
est2 = GroupLasso(alpha=0.)
est3 = DirtyModel(alpha=0., beta=0.)
est4 = MultiLevelLasso(alpha=0.)
est5 = ReMTW(alpha=0., beta=0.)
est6 = MTW(alpha=0., beta=0.)
models = [est0, est1, est2, est3, est4, est5, est6]
for model in models:
with pytest.warns(UserWarning, match='LinearRegression'):
model.fit(X, y)
def test_slow_convergence(gaussian_data):
X, y = gaussian_data
max_iter = 1
est0 = IndRewLasso(alpha=len(X) * [0.01], max_iter=max_iter)
est1 = IndLasso(alpha=len(X) * [0.01], max_iter=max_iter)
est2 = GroupLasso(alpha=0.01, max_iter=max_iter)
est3 = DirtyModel(alpha=0.01, beta=0.01, max_iter=max_iter)
est4 = MultiLevelLasso(alpha=0.01, max_iter=max_iter)
est5 = ReMTW(alpha=0.01, beta=0.01, max_iter=max_iter)
est6 = MTW(alpha=0.01, beta=0.01, max_iter=max_iter)
models = [est0, est1, est2, est3, est4, est5, est6]
for model in models:
with pytest.warns(ConvergenceWarning, match='number of iterations'):
model.fit(X, y)
def test_set_params_gl(gaussian_data):
X, y = gaussian_data
n_samples = y.shape[1]
Xty = np.array([xx.T.dot(yy) for xx, yy in zip(X, y)])
alpha_max = np.linalg.norm(Xty, axis=0).max()
alpha1 = 0.05 * alpha_max / n_samples
alpha2 = 0.6 * alpha_max / n_samples
coef0 = GroupLasso(alpha=alpha1).fit(X, y).coef_
est = GroupLasso(alpha=alpha2)
est.fit(X, y)
est.set_params(alpha=alpha1)
coef1 = est.fit(X, y).coef_
assert_allclose(coef0, coef1, 1e-4)
# add noise y += 0.2 * np.std(y) + rng.randn(n_tasks, n_samples) ########################################################## # Lasso fit alpha = 0.5 * np.ones(n_tasks) lasso = IndLasso(alpha=alpha) lasso.fit(X, y) ########################################################## # Group Lasso fit alpha = 1.75 gl = GroupLasso(alpha=alpha) gl.fit(X, y) ########################################################## # Dirty models fit alpha = 0.8 beta = 0.4 dirty = DirtyModel(alpha=alpha, beta=beta) dirty.fit(X, y) ########################################################## # Multilevel Lasso fit alpha = 0.25 mll = MultiLevelLasso(alpha=alpha)