def test_combo_overlapping_smooth(self): import numpy as np from parsimony.functions import CombinedFunction import parsimony.algorithms.proximal as proximal import parsimony.functions as functions import parsimony.functions.nesterov.gl as gl import parsimony.datasets.simulate.l1_l2_glmu as l1_l2_glmu import parsimony.utils.start_vectors as start_vectors np.random.seed(314) # Note that p must be even! n, p = 25, 30 groups = [range(0, 2 * p / 3), range(p / 3, p)] weights = [1.5, 0.5] A = gl.A_from_groups(p, groups=groups, weights=weights) l = 0.618 k = 1.0 - l g = 2.718 start_vector = start_vectors.RandomStartVector(normalise=True) beta = start_vector.get_vector(p) alpha = 1.0 Sigma = alpha * np.eye(p, p) \ + (1.0 - alpha) * np.random.randn(p, p) mean = np.zeros(p) M = np.random.multivariate_normal(mean, Sigma, n) e = np.random.randn(n, 1) snr = 100.0 mu_min = 5e-8 X, y, beta_star = l1_l2_glmu.load(l, k, g, beta, M, e, A, mu=mu_min, snr=snr) eps = 1e-8 max_iter = 5000 beta_start = start_vector.get_vector(p) mus = [5e-0, 5e-2, 5e-4, 5e-6, 5e-8] fista = proximal.FISTA(eps=eps, max_iter=max_iter / len(mus)) beta_parsimony = beta_start for mu in mus: # function = functions.LinearRegressionL1L2GL(X, y, l, k, g, # A=A, mu=mu, # penalty_start=0) function = CombinedFunction() function.add_function(functions.losses.LinearRegression(X, y, mean=False)) function.add_penalty(functions.penalties.L2Squared(l=k)) function.add_penalty(gl.GroupLassoOverlap(l=g, A=A, mu=mu, penalty_start=0)) function.add_prox(functions.penalties.L1(l=l)) beta_parsimony = fista.run(function, beta_parsimony) berr = np.linalg.norm(beta_parsimony - beta_star) # print berr assert berr < 5e-3 f_parsimony = function.f(beta_parsimony) f_star = function.f(beta_star) # print abs(f_parsimony - f_star) assert abs(f_parsimony - f_star) < 5e-7
def test_nonoverlapping_smooth(self): # Spams: http://spams-devel.gforge.inria.fr/doc-python/doc_spams.pdf import numpy as np from parsimony.functions import CombinedFunction import parsimony.algorithms.proximal as proximal import parsimony.functions as functions import parsimony.functions.nesterov.gl as gl import parsimony.datasets.simulate.l1_l2_glmu as l1_l2_glmu import parsimony.utils.start_vectors as start_vectors np.random.seed(42) # Note that p must be even! n, p = 25, 20 groups = [range(0, p / 2), range(p / 2, p)] # weights = [1.5, 0.5] A = gl.A_from_groups(p, groups=groups) # , weights=weights) l = 0.0 k = 0.0 g = 0.9 start_vector = start_vectors.RandomStartVector(normalise=True) beta = start_vector.get_vector(p) alpha = 1.0 Sigma = alpha * np.eye(p, p) \ + (1.0 - alpha) * np.random.randn(p, p) mean = np.zeros(p) M = np.random.multivariate_normal(mean, Sigma, n) e = np.random.randn(n, 1) snr = 100.0 mu_min = 5e-8 X, y, beta_star = l1_l2_glmu.load(l, k, g, beta, M, e, A, mu=mu_min, snr=snr) eps = 1e-8 max_iter = 18000 beta_start = start_vector.get_vector(p) mus = [5e-0, 5e-2, 5e-4, 5e-6, 5e-8] fista = proximal.FISTA(eps=eps, max_iter=max_iter / len(mus)) beta_parsimony = beta_start for mu in mus: # function = functions.LinearRegressionL1L2GL(X, y, l, k, g, # A=A, mu=mu, # penalty_start=0) function = CombinedFunction() function.add_function(functions.losses.LinearRegression(X, y, mean=False)) function.add_penalty(gl.GroupLassoOverlap(l=g, A=A, mu=mu, penalty_start=0)) beta_parsimony = fista.run(function, beta_parsimony) try: import spams params = {"loss": "square", "regul": "group-lasso-l2", "groups": np.array([1] * (p / 2) + [2] * (p / 2), dtype=np.int32), "lambda1": g, "max_it": max_iter, "tol": eps, "ista": False, "numThreads": -1, } beta_spams, optim_info = \ spams.fistaFlat(Y=np.asfortranarray(y), X=np.asfortranarray(X), W0=np.asfortranarray(beta_start), return_optim_info=True, **params) # print beta_spams except ImportError: beta_spams = np.asarray([[15.56784201], [39.51679274], [30.42583205], [24.8816362], [6.48671072], [6.48350546], [2.41477318], [36.00285723], [24.98522184], [29.43128643], [0.85520539], [40.31463542], [34.60084146], [8.82322513], [7.55741642], [7.62364398], [12.64594707], [21.81113869], [17.95400007], [12.10507338]]) berr = np.linalg.norm(beta_parsimony - beta_spams) # print berr assert berr < 5e-3 f_parsimony = function.f(beta_parsimony) f_spams = function.f(beta_spams) ferr = abs(f_parsimony - f_spams) # print ferr assert ferr < 5e-6
def test_nonoverlapping_smooth(self): # Spams: http://spams-devel.gforge.inria.fr/doc-python/doc_spams.pdf import numpy as np from parsimony.functions import CombinedFunction import parsimony.algorithms.proximal as proximal import parsimony.functions as functions import parsimony.functions.nesterov.gl as gl import parsimony.datasets.simulate.l1_l2_glmu as l1_l2_glmu import parsimony.utils.weights as weights np.random.seed(42) # Note that p must be even! n, p = 25, 20 groups = [list(range(0, int(p / 2))), list(range(int(p / 2), p))] # weights = [1.5, 0.5] A = gl.linear_operator_from_groups(p, groups=groups) # , weights=weights) l = 0.0 k = 0.0 g = 0.9 start_vector = weights.RandomUniformWeights(normalise=True) beta = start_vector.get_weights(p) alpha = 1.0 Sigma = alpha * np.eye(p, p) \ + (1.0 - alpha) * np.random.randn(p, p) mean = np.zeros(p) M = np.random.multivariate_normal(mean, Sigma, n) e = np.random.randn(n, 1) snr = 100.0 mu_min = 5e-8 X, y, beta_star = l1_l2_glmu.load(l, k, g, beta, M, e, A, mu=mu_min, snr=snr) eps = 1e-8 max_iter = 18000 beta_start = start_vector.get_weights(p) mus = [5e-0, 5e-2, 5e-4, 5e-6, 5e-8] fista = proximal.FISTA(eps=eps, max_iter=max_iter / len(mus)) beta_parsimony = beta_start for mu in mus: # function = functions.LinearRegressionL1L2GL(X, y, l, k, g, # A=A, mu=mu, # penalty_start=0) function = CombinedFunction() function.add_loss( functions.losses.LinearRegression(X, y, mean=False)) function.add_penalty( gl.GroupLassoOverlap(l=g, A=A, mu=mu, penalty_start=0)) beta_parsimony = fista.run(function, beta_parsimony) try: import spams params = { "loss": "square", "regul": "group-lasso-l2", "groups": np.array([1] * (int(p / 2)) + [2] * (int(p / 2)), dtype=np.int32), "lambda1": g, "max_it": max_iter, "tol": eps, "ista": False, "numThreads": -1, } beta_spams, optim_info = \ spams.fistaFlat(Y=np.asfortranarray(y), X=np.asfortranarray(X), W0=np.asfortranarray(beta_start), return_optim_info=True, **params) # print beta_spams except ImportError: # beta_spams = np.asarray([[15.56784201], # [39.51679274], # [30.42583205], # [24.8816362], # [6.48671072], # [6.48350546], # [2.41477318], # [36.00285723], # [24.98522184], # [29.43128643], # [0.85520539], # [40.31463542], # [34.60084146], # [8.82322513], # [7.55741642], # [7.62364398], # [12.64594707], # [21.81113869], # [17.95400007], # [12.10507338]]) beta_spams = np.asarray([[-11.93855944], [42.889350930], [22.076438880], [9.3869208300], [-32.73310431], [-32.73509107], [-42.05298794], [34.844819990], [9.6210946300], [19.799892400], [-45.62041548], [44.716039010], [31.634706630], [-27.37416567], [-30.27711859], [-30.12673231], [-18.62803747], [2.3561952400], [-6.476922020], [-19.86630857]]) berr = np.linalg.norm(beta_parsimony - beta_spams) # print berr assert berr < 5e-3 f_parsimony = function.f(beta_parsimony) f_spams = function.f(beta_spams) ferr = abs(f_parsimony - f_spams) # print ferr assert ferr < 5e-6
def test_combo_overlapping_smooth(self): import numpy as np from parsimony.functions import CombinedFunction import parsimony.algorithms.proximal as proximal import parsimony.functions as functions import parsimony.functions.nesterov.gl as gl import parsimony.datasets.simulate.l1_l2_glmu as l1_l2_glmu import parsimony.utils.weights as weights np.random.seed(314) # Note that p must be even! n, p = 25, 30 groups = [list(range(0, 2 * int(p / 3))), list(range(int(p / 3), p))] group_weights = [1.5, 0.5] A = gl.linear_operator_from_groups(p, groups=groups, weights=group_weights) l = 0.618 k = 1.0 - l g = 2.718 start_vector = weights.RandomUniformWeights(normalise=True) beta = start_vector.get_weights(p) alpha = 1.0 Sigma = alpha * np.eye(p, p) \ + (1.0 - alpha) * np.random.randn(p, p) mean = np.zeros(p) M = np.random.multivariate_normal(mean, Sigma, n) e = np.random.randn(n, 1) snr = 100.0 mu_min = 5e-8 X, y, beta_star = l1_l2_glmu.load(l, k, g, beta, M, e, A, mu=mu_min, snr=snr) eps = 1e-8 max_iter = 5000 beta_start = start_vector.get_weights(p) mus = [5e-0, 5e-2, 5e-4, 5e-6, 5e-8] fista = proximal.FISTA(eps=eps, max_iter=max_iter / len(mus)) beta_parsimony = beta_start for mu in mus: # function = functions.LinearRegressionL1L2GL(X, y, l, k, g, # A=A, mu=mu, # penalty_start=0) function = CombinedFunction() function.add_loss( functions.losses.LinearRegression(X, y, mean=False)) function.add_penalty(functions.penalties.L2Squared(l=k)) function.add_penalty( gl.GroupLassoOverlap(l=g, A=A, mu=mu, penalty_start=0)) function.add_prox(functions.penalties.L1(l=l)) beta_parsimony = fista.run(function, beta_parsimony) berr = np.linalg.norm(beta_parsimony - beta_star) # print berr assert berr < 5e-3 f_parsimony = function.f(beta_parsimony) f_star = function.f(beta_star) # print abs(f_parsimony - f_star) assert abs(f_parsimony - f_star) < 5e-7
def test_overlapping_smooth(self): import numpy as np from parsimony.functions import CombinedFunction import parsimony.functions as functions import parsimony.functions.nesterov.gl as gl import parsimony.datasets.simulate.l1_l2_glmu as l1_l2_glmu import parsimony.utils.weights as weights np.random.seed(314) # Note that p must be even! n, p = 25, 30 groups = [list(range(0, 2 * int(p / 3))), list(range(int(p / 3), p))] group_weights = [1.5, 0.5] A = gl.linear_operator_from_groups(p, groups=groups, weights=group_weights) l = 0.0 k = 0.0 g = 0.9 start_vector = weights.RandomUniformWeights(normalise=True) beta = start_vector.get_weights(p) alpha = 1.0 Sigma = alpha * np.eye(p, p) \ + (1.0 - alpha) * np.random.randn(p, p) mean = np.zeros(p) M = np.random.multivariate_normal(mean, Sigma, n) e = np.random.randn(n, 1) snr = 100.0 mu_min = 5e-8 X, y, beta_star = l1_l2_glmu.load(l, k, g, beta, M, e, A, mu=mu_min, snr=snr) eps = 1e-8 max_iter = 15000 beta_start = start_vector.get_weights(p) mus = [5e-0, 5e-2, 5e-4, 5e-6, 5e-8] fista = FISTA(eps=eps, max_iter=max_iter / len(mus)) beta_parsimony = beta_start for mu in mus: # function = functions.LinearRegressionL1L2GL(X, y, l, k, g, # A=A, mu=mu, # penalty_start=0) function = CombinedFunction() function.add_loss(functions.losses.LinearRegression(X, y, mean=False)) function.add_penalty(gl.GroupLassoOverlap(l=g, A=A, mu=mu, penalty_start=0)) beta_parsimony = fista.run(function, beta_parsimony) berr = np.linalg.norm(beta_parsimony - beta_star) # print berr assert berr < 5e-2 f_parsimony = function.f(beta_parsimony) f_star = function.f(beta_star) # print(abs(f_parsimony - f_star)) assert abs(f_parsimony - f_star) < 5e-6