def test_weights_group_lasso():
n_samples, n_features = 30, 50
X, y = build_dataset(n_samples, n_features, sparse_X=True)
groups = 5
n_groups = n_features // groups
np.random.seed(0)
weights = np.abs(np.random.randn(n_groups))
tol = 1e-14
params = {'n_alphas': 10, 'tol': tol, 'verbose': 1}
augmented_weights = np.repeat(weights, groups)
alphas1, coefs1, gaps1 = celer_path(
X, y, "grouplasso", groups=groups, weights=weights,
eps=1e-2, **params)
alphas2, coefs2, gaps2 = celer_path(
X.multiply(1 / augmented_weights[None, :]), y, "grouplasso",
groups=groups, eps=1e-2, **params)
assert_allclose(alphas1, alphas2)
assert_allclose(
coefs1, coefs2 / augmented_weights[:, None], rtol=1e-3)
assert_array_less(gaps1, tol * norm(y) ** 2 / len(y))
assert_array_less(gaps2, tol * norm(y) ** 2 / len(y))
def test_group_lasso_path(sparse_X):
n_features = 50
X, y = build_dataset(
n_samples=11, n_features=n_features, sparse_X=sparse_X)
alphas, coefs, gaps = celer_path(
X, y, "grouplasso", groups=5, eps=1e-2, n_alphas=10, tol=1e-8)
tol = 1e-8
np.testing.assert_array_less(gaps, tol)
def plot_varying_sigma(corr, density, snr, max_iter=100):
np.random.seed(0)
# true coefficient vector has entries equal to 0 or 1
supp = np.random.choice(n_features, size=int(density * n_features),
replace=False)
w_true = np.zeros(n_features)
w_true[supp] = 1
X_, y_, w_true = make_correlated_data(
n_samples=int(n_samples * 4 / 3.), n_features=n_features,
w_true=w_true,
corr=corr, snr=snr, random_state=0)
X, X_test, y, y_test = train_test_split(X_, y_, test_size=0.25)
print('Starting computation for this setting')
ratio = 10 * datadriven_ratio(X, y)
_, _, _, all_w = dual_primal(
X, y, step_ratio=ratio, rho=0.99, ret_all=True,
max_iter=max_iter,
f_store=1)
fig, axarr = plt.subplots(2, 2, sharey='row', sharex='col',
figsize=(4.2, 3.5), constrained_layout=True)
scores = [f1_score(w != 0, w_true != 0) for w in all_w]
mses = np.array([mean_squared_error(y_test, X_test @ w) for w in all_w])
axarr[0, 0].plot(scores)
axarr[1, 0].plot(mses / np.mean(y_test ** 2))
axarr[0, 0].set_ylim(0, 1)
axarr[0, 0].set_ylabel('F1 score')
axarr[1, 0].set_ylabel("pred MSE left out")
axarr[-1, 0].set_xlabel("CP iteration")
axarr[0, 0].set_title('Iterative regularization')
# last column: Lasso results
alphas = norm(X.T @ y, ord=np.inf) / len(y) * np.geomspace(1, 1e-3)
coefs = celer_path(X, y, 'lasso', alphas=alphas)[1].T
axarr[0, 1].semilogx(
alphas, [f1_score(coef != 0, w_true != 0) for coef in coefs])
axarr[1, 1].semilogx(
alphas,
np.array([mean_squared_error(y_test, X_test @ coef) for coef in coefs])
/ np.mean(y_test ** 2))
axarr[-1, 1].set_xlabel(r'$\lambda$')
axarr[0, 1].set_title("Lasso path")
axarr[0, 1].invert_xaxis()
plt.show(block=False)
return fig
def test_group_lasso_path(sparse_X):
n_features = 50
X, y = build_dataset(n_samples=11,
n_features=n_features,
sparse_X=sparse_X,
n_informative_features=n_features)[:2]
alphas, coefs, gaps = celer_path(X,
y,
"grouplasso",
groups=5,
eps=1e-2,
n_alphas=10,
tol=1e-8)
tol = 1e-8
np.testing.assert_array_less(gaps, tol)
check_estimator(GroupLasso)
def plot_varying_sigma(corr, density, snr, steps, max_iter=100, rho=0.99):
np.random.seed(0)
# true coefficient vector has entries equal to 0 or 1
supp = np.random.choice(n_features,
size=int(density * n_features),
replace=False)
w_true = np.zeros(n_features)
w_true[supp] = 1
X_, y_, w_true = make_correlated_data(n_samples=int(n_samples * 4 / 3.),
n_features=n_features,
w_true=w_true,
corr=corr,
snr=snr,
random_state=0)
X, X_test, y, y_test = train_test_split(X_, y_, test_size=0.25)
print('Starting computation for this setting')
fig, axarr = plt.subplots(4,
2,
sharey='row',
sharex='col',
figsize=(7, 5),
constrained_layout=True)
fig.suptitle(r"Correlation=%.1f, $||w^*||_0$= %s, snr=%s" %
(corr, (w_true != 0).sum(), snr))
for i, step in enumerate(steps):
_, _, _, all_w = dual_primal(X,
y,
step=step,
rho=rho,
ret_all=True,
max_iter=max_iter,
f_store=1)
scores = [f1_score(w != 0, w_true != 0) for w in all_w]
supp_size = np.sum(all_w != 0, axis=1)
mses = [mean_squared_error(y_test, X_test @ w) for w in all_w]
axarr[0, 0].plot(scores, label=r"$\sigma=1 /%d ||X||$" % step)
axarr[1, 0].semilogy(supp_size)
axarr[2, 0].plot(norm(all_w - w_true, axis=1))
axarr[3, 0].plot(mses)
axarr[0, 0].set_ylim(0, 1)
axarr[0, 0].set_ylabel('F1 score for support')
axarr[1, 0].set_ylabel(r"$||w_k||_0$")
axarr[2, 0].set_ylabel(r'$\Vert w_k - w^*\Vert$')
axarr[2, 0].set_xlabel("CP iteration")
axarr[3, 0].set_ylabel("pred MSE left out")
axarr[0, 0].legend(loc='lower right', fontsize=10)
axarr[0, 0].set_title('Iterative regularization')
# last column: Lasso results
alphas = norm(X.T @ y, ord=np.inf) / len(y) * np.geomspace(1, 1e-3)
coefs = celer_path(X, y, 'lasso', alphas=alphas)[1].T
axarr[0, 1].semilogx(alphas,
[f1_score(coef != 0, w_true != 0) for coef in coefs])
axarr[1, 1].semilogx(alphas, [np.sum(coef != 0) for coef in coefs])
axarr[2, 1].semilogx(alphas, [norm(coef - w_true) for coef in coefs])
axarr[3, 1].semilogx(
alphas, [mean_squared_error(y_test, X_test @ coef) for coef in coefs])
axarr[3, 1].set_xlabel(r'$\lambda$')
axarr[0, 1].set_title("Lasso path")
for i in range(3):
axarr[i, 1].set_xlim(*axarr[i, 1].get_xlim()[::-1])
plt.show(block=False)