| """Sparse-group recovery: exact-support recovery on clean synthetic data.""" |
| import numpy as np |
| from spectra_rsi.recovery import sparse_group_recover, debias, active_groups |
|
|
|
|
| def test_group_recovery_identifies_support(): |
| rng = np.random.default_rng(0) |
| p, n_groups = 32, 8 |
| groups = [np.arange(i * 4, (i + 1) * 4) for i in range(n_groups)] |
| x_true = np.zeros(p) |
| x_true[groups[2]] = rng.normal(0, 1, 4) |
| x_true[groups[5]] = rng.normal(0, 1, 4) |
| M = rng.normal(0, 1 / np.sqrt(60), (60, p)) |
| y = M @ x_true + 0.01 * rng.normal(size=60) |
| gw = np.ones(n_groups) |
| x_hat = sparse_group_recover(y, M, groups, gw, 1e-3, 5e-3, n_iters=800) |
| x_deb, _ = debias(y, M, x_hat) |
| act, _ = active_groups(x_deb, groups, rel_tol=0.1) |
| assert set(act) == {2, 5} |
| assert np.linalg.norm(x_deb - x_true) / np.linalg.norm(x_true) < 0.1 |
|
|
