def test_ModelLogReg(self):
"""...Numerical consistency check of loss and gradient for Logistic
Regression
"""
np.random.seed(12)
n_samples, n_features = 5000, 10
w0 = np.random.randn(n_features)
c0 = np.random.randn()
# First check with intercept
X, y = SimuLogReg(w0, c0, n_samples=n_samples, verbose=False,
dtype=self.dtype).simulate()
X_spars = csr_matrix(X, dtype=self.dtype)
model = ModelLogReg(fit_intercept=True).fit(X, y)
model_spars = ModelLogReg(fit_intercept=True).fit(X_spars, y)
self.run_test_for_glm(model, model_spars)
self._test_glm_intercept_vs_hardcoded_intercept(model)
# Then check without intercept
X, y = SimuLogReg(w0, None, n_samples=n_samples, verbose=False,
seed=2038, dtype=self.dtype).simulate()
X_spars = csr_matrix(X, dtype=self.dtype)
model = ModelLogReg(fit_intercept=False).fit(X, y)
model_spars = ModelLogReg(fit_intercept=False).fit(X_spars, y)
self.run_test_for_glm(model, model_spars)
self._test_glm_intercept_vs_hardcoded_intercept(model)
# Test for the Lipschitz constants without intercept
self.assertAlmostEqual(model.get_lip_best(), 0.67184209642814952,
places=self.decimal_places)
self.assertAlmostEqual(model.get_lip_mean(), 2.48961431697108,
places=self.decimal_places)
self.assertAlmostEqual(model.get_lip_max(), 13.706542412138093,
places=self.decimal_places)
self.assertAlmostEqual(model_spars.get_lip_mean(),
model.get_lip_mean(),
places=self.decimal_places)
self.assertAlmostEqual(model_spars.get_lip_max(), model.get_lip_max(),
places=self.decimal_places)
# Test for the Lipschitz constants with intercept
model = ModelLogReg(fit_intercept=True).fit(X, y)
model_spars = ModelLogReg(fit_intercept=True).fit(X_spars, y)
self.assertAlmostEqual(model.get_lip_best(), 0.671892096428,
places=self.decimal_places)
self.assertAlmostEqual(model.get_lip_mean(), 2.739614316971082,
places=self.decimal_places)
self.assertAlmostEqual(model.get_lip_max(), 13.956542412138093,
places=self.decimal_places)
self.assertAlmostEqual(model_spars.get_lip_mean(),
model.get_lip_mean(),
places=self.decimal_places)
self.assertAlmostEqual(model_spars.get_lip_max(), model.get_lip_max(),
places=self.decimal_places)
from tick.prox import ProxElasticNet, ProxL1
from tick.plot import plot_history
n_samples, n_features, = 5000, 50
weights0 = weights_sparse_gauss(n_features, nnz=10)
intercept0 = 0.2
X, y = SimuLogReg(weights=weights0,
intercept=intercept0,
n_samples=n_samples,
seed=123,
verbose=False).simulate()
model = ModelLogReg(fit_intercept=True).fit(X, y)
prox = ProxElasticNet(strength=1e-3, ratio=0.5, range=(0, n_features))
solver_params = {'max_iter': 100, 'tol': 0., 'verbose': False}
x0 = np.zeros(model.n_coeffs)
gd = GD(linesearch=False, **solver_params).set_model(model).set_prox(prox)
gd.solve(x0, step=1 / model.get_lip_best())
agd = AGD(linesearch=False, **solver_params).set_model(model).set_prox(prox)
agd.solve(x0, step=1 / model.get_lip_best())
sgd = SGD(**solver_params).set_model(model).set_prox(prox)
sgd.solve(x0, step=500.)
svrg = SVRG(**solver_params).set_model(model).set_prox(prox)
svrg.solve(x0, step=1 / model.get_lip_max())
plot_history([gd, agd, sgd, svrg], log_scale=True, dist_min=True)
