def test_group_lasso(l1=0.1, **control): """ fits a fused lasso as a group lasso approximator, i.e. all 2norms are one dimensional """ Y = np.load('Y.npy') n = Y.shape[0] def e(i, n): z = np.zeros(n) z[i] = 1. z[i + 1] = -1 return z Dv = [(e(i, n), l1 * n) for i in range(n - 1)] D = (np.identity(n) - np.diag(np.ones(n - 1), -1))[1:] M = np.linalg.eigvalsh(np.dot(D.T, D)).max() p1 = group.group_approximator((Dv, Y), L=M) p2 = group.group_lasso((np.identity(n), Dv, Y), L=M) p3 = signal_approximator.signal_approximator((D, Y), L=M) p3.assign_penalty(l1=l1 * n) t1 = time.time() opt1 = regreg.FISTA(p1) opt1.debug = True opt1.fit(tol=control['tol'], max_its=control['max_its']) t2 = time.time() ts1 = t2 - t1 t1 = time.time() opt2 = regreg.FISTA(p3) opt2.fit(tol=control['tol'], max_its=control['max_its']) t2 = time.time() ts3 = t2 - t1 t1 = time.time() opt3 = regreg.FISTA(p3) opt3.fit(tol=control['tol'], max_its=control['max_its']) t2 = time.time() ts3 = t2 - t1 beta1, _ = opt1.output beta2, _ = opt2.output beta3, _ = opt3.output X = np.arange(n) nose.tools.assert_true( (np.fabs(beta1 - beta3).sum() / np.fabs(beta1).sum()) < 1.0e-04) nose.tools.assert_true( (np.fabs(beta1 - beta2).sum() / np.fabs(beta1).sum()) < 1.0e-04)
def test_group_lasso_approximator1(l1=0.1, **control): Y = np.load('Y.npy') n = Y.shape[0] def e(i, n): z = np.zeros(n) z[i] = 1. return z Dv = [(e(i, n), l1 * n) for i in range(n)] p1 = group.group_approximator((Dv, Y)) p2 = lasso.gengrad((np.identity(n), Y)) p2.assign_penalty(l1=l1 * n) p3 = signal_approximator.signal_approximator((np.identity(n), Y)) p3.assign_penalty(l1=l1 * n) t1 = time.time() opt1 = regreg.FISTA(p1) opt1.debug = True opt1.fit(tol=control['tol'], max_its=control['max_its']) t2 = time.time() ts1 = t2 - t1 t1 = time.time() opt2 = regreg.FISTA(p2) opt2.fit(tol=control['tol'], max_its=control['max_its']) t2 = time.time() ts2 = t2 - t1 t1 = time.time() opt3 = regreg.FISTA(p3) opt3.fit(tol=control['tol'], max_its=control['max_its']) t2 = time.time() ts3 = t2 - t1 beta1, _ = opt1.output beta2, _ = opt2.output beta3, _ = opt3.output X = np.arange(n) nose.tools.assert_true( (np.fabs(beta1 - beta3).sum() / np.fabs(beta1).sum()) < 1.0e-04) nose.tools.assert_true( (np.fabs(beta1 - beta2).sum() / np.fabs(beta1).sum()) < 1.0e-04)