def logistic_regression_large(self, alpha, mu):
X, w0, gt = self._problem(n=1000,
nlf=100,
lf_prior=0.2,
lf_mean=0.7,
lf_sd=0.25,
nf=5000,
f_prior=0.05,
f_mean=0.6,
f_sd=0.25)
l = LogReg()
l.train(X=X,
n_iter=1000,
tol=1e-4,
w0=w0,
sample=False,
alpha=alpha,
mu=mu,
rate=0.01,
verbose=True)
self.assertGreater(np.mean(np.sign(X.dot(l.w)) == gt), 0.85)
def test_logistic_regression_sample(self):
print("Running logistic regression test with sparse operations")
X, w0, gt = self._problem(n=500,
nlf=75,
lf_prior=0.3,
lf_mean=0.7,
lf_sd=0.25,
nf=1000,
f_prior=0.1,
f_mean=0.6,
f_sd=0.25)
mu = 1e-4
l_d = LogReg()
l_d.train(X=X,
n_iter=2500,
tol=1e-4,
w0=w0,
sample=False,
alpha=self.ridge,
mu=mu,
rate=0.01,
verbose=True)
l_s = LogReg()
l_s.train(X=X,
n_iter=2500,
tol=1e-4,
w0=w0,
sample=True,
n_samples=200,
alpha=self.ridge,
mu=mu,
rate=0.01,
verbose=True)
# Check sample marginals are close to deterministic solutio
ld, ls = odds_to_prob(X.dot(l_d.w)), odds_to_prob(X.dot(l_s.w))
self.assertLessEqual(
np.linalg.norm(ld - ls) / np.linalg.norm(ld), 0.05)