def test_asaga_solver(self):
"""...Check ASAGA solver for a Logistic Regression with Elastic net
penalization
"""
seed = 1398
np.random.seed(seed)
n_samples = 4000
n_features = 30
weights = weights_sparse_gauss(n_features, nnz=3).astype(self.dtype)
intercept = 0.2
penalty_strength = 1e-3
sparsity = 1e-4
features = sparse.rand(n_samples, n_features, density=sparsity,
format='csr', random_state=8).astype(self.dtype)
simulator = SimuLogReg(weights, n_samples=n_samples, features=features,
verbose=False, intercept=intercept,
dtype=self.dtype)
features, labels = simulator.simulate()
model = ModelLogReg(fit_intercept=True)
model.fit(features, labels)
prox = ProxElasticNet(penalty_strength, ratio=0.1, range=(0,
n_features))
solver_step = 1. / model.get_lip_max()
saga = SAGA(step=solver_step, max_iter=100, tol=1e-10, verbose=False,
n_threads=1, record_every=10, seed=seed)
saga.set_model(model).set_prox(prox)
saga.solve()
asaga = SAGA(step=solver_step, max_iter=100, tol=1e-10, verbose=False,
n_threads=2, record_every=10, seed=seed)
asaga.set_model(model).set_prox(prox)
asaga.solve()
np.testing.assert_array_almost_equal(saga.solution, asaga.solution,
decimal=4)
self.assertGreater(np.linalg.norm(saga.solution[:-1]), 0)
#!/usr/bin/python3
# expect tick first on PYTHONPATH
from tick.array.build.array import tick_double_sparse2d_from_file, tick_double_array_from_file
from tick.prox import ProxL2Sq; from tick.solver import SAGA; from tick.linear_model import ModelLogReg
X = tick_double_sparse2d_from_file("url.features.cereal")
n_samples = X.shape[0]; n_features = X.shape[1]
y = tick_double_array_from_file ("url.labels.cereal")
model = ModelLogReg(fit_intercept=False).fit(X, y)
prox = ProxL2Sq((1. / n_samples) + 1e-10, range=(0, n_features))
asaga = SAGA(step=0.00257480411965, max_iter=200, tol=1e-10, verbose=False,
n_threads=8, log_every_n_epochs=10)
asaga.set_model(model).set_prox(prox)
asaga.solve()
asaga.print_history()
