def test_regularized(self):
np.random.seed(3453)
exog = np.random.normal(size=(400, 5))
groups = np.kron(np.arange(100), np.ones(4))
expected_endog = exog[:, 0] - exog[:, 2]
endog = expected_endog + np.kron(np.random.normal(size=100), np.ones(4)) + np.random.normal(size=400)
# L1 regularization
md = MixedLM(endog, exog, groups)
mdf1 = md.fit_regularized(alpha=1.0)
mdf1.summary()
# L1 regularization
md = MixedLM(endog, exog, groups)
mdf2 = md.fit_regularized(alpha=10 * np.ones(5))
mdf2.summary()
# L2 regularization
pen = penalties.L2()
mdf3 = md.fit_regularized(method=pen, alpha=0.0)
mdf3.summary()
# L2 regularization
pen = penalties.L2()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mdf4 = md.fit_regularized(method=pen, alpha=100.0)
mdf4.summary()
# Pseudo-Huber regularization
pen = penalties.PseudoHuber(0.3)
mdf5 = md.fit_regularized(method=pen, alpha=1.0)
mdf5.summary()
def test_regularized(self):
np.random.seed(3453)
exog = np.random.normal(size=(400, 5))
groups = np.kron(np.arange(100), np.ones(4))
expected_endog = exog[:, 0] - exog[:, 2]
endog = expected_endog +\
np.kron(np.random.normal(size=100), np.ones(4)) +\
np.random.normal(size=400)
# L1 regularization
md = MixedLM(endog, exog, groups)
mdf1 = md.fit_regularized(alpha=1.)
mdf1.summary()
# L1 regularization
md = MixedLM(endog, exog, groups)
mdf2 = md.fit_regularized(alpha=10 * np.ones(5))
mdf2.summary()
# L2 regularization
pen = penalties.L2()
mdf3 = md.fit_regularized(method=pen, alpha=0.)
mdf3.summary()
# L2 regularization
pen = penalties.L2()
mdf4 = md.fit_regularized(method=pen, alpha=100.)
mdf4.summary()
# Pseudo-Huber regularization
pen = penalties.PseudoHuber(0.3)
mdf4 = md.fit_regularized(method=pen, alpha=1.)
mdf4.summary()
