def _initialize(cls):
y, x = cls.y, cls.x
# adding 10 to avoid strict rtol at predicted values close to zero
y = y + 10
k = x.shape[1]
cov_type = 'HC0'
restriction = np.eye(k)[2:]
modp = TheilGLS(y, x, r_matrix=restriction)
# the corresponding Theil penweight seems to be 2 * nobs / sigma2_e
cls.res2 = modp.fit(pen_weight=120.74564413221599 * 1000, use_t=False)
pen = smpen.L2ContraintsPenalty(restriction=restriction)
mod = GLMPenalized(y, x, family=family.Gaussian(), penal=pen)
# use default weight for GLMPenalized
mod.pen_weight *= 1
cls.res1 = mod.fit(cov_type=cov_type,
method='bfgs',
maxiter=100,
disp=0,
trim=False)
cls.k_nonzero = k
cls.exog_index = slice(None, cls.k_nonzero, None)
cls.k_params = x.shape[1]
cls.atol = 1e-5
cls.rtol = 1e-5
def _initialize(cls):
y, x = cls.y, cls.x
# adding 10 to avoid strict rtol at predicted values close to zero
y = y + 10
cov_type = 'HC0'
modp = GLM(y, x[:, :cls.k_nonzero], family=family.Gaussian())
cls.res2 = modp.fit(cov_type=cov_type,
method='bfgs',
maxiter=100,
disp=0)
weights = (np.arange(x.shape[1]) >= 4).astype(float)
mod = GLMPenalized(y,
x,
family=family.Gaussian(),
penal=smpen.L2ContraintsPenalty(weights=weights))
# make pen_weight large to force redundant to close to zero
mod.pen_weight *= 500
cls.res1 = mod.fit(cov_type=cov_type,
method='bfgs',
maxiter=100,
disp=0,
trim=False)
cls.exog_index = slice(None, cls.k_nonzero, None)
cls.k_params = x.shape[1]
cls.atol = 1e-5
cls.rtol = 1e-5
def test_equivalence(self):
# compare plain penalty with included weights or restriction
pen = self.pen
x = self.params
k = x.shape[1]
pen2 = smpen.L2ContraintsPenalty(weights=np.ones(k))
pen3 = smpen.L2ContraintsPenalty(restriction=np.eye(k))
f = pen.func(x.T)
d = pen.deriv(x.T)
d2 = np.array([pen.deriv2(np.atleast_1d(xi)) for xi in x])
for pen_ in [pen2, pen3]:
assert_allclose(pen_.func(x.T), f, rtol=1e-7, atol=1e-8)
assert_allclose(pen_.deriv(x.T), d, rtol=1e-7, atol=1e-8)
d2_ = np.array([pen.deriv2(np.atleast_1d(xi)) for xi in x])
assert_allclose(d2_, d2, rtol=1e-10, atol=1e-8)
def setup_class(cls):
exog, penalty_matrix, restriction = cls._init()
endog = data_mcycle['accel']
pen = smpen.L2ContraintsPenalty(restriction=restriction)
mod = GLMPenalized(endog, exog, family=family.Gaussian(),
penal=pen)
# scaling of penweight in R mgcv
s_scale_r = 0.02630734
# set pen_weight to correspond to R mgcv example
cls.pw = mod.pen_weight = 1 / s_scale_r / 2
cls.res1 = mod.fit(cov_type=cls.cov_type, method='bfgs', maxiter=100,
disp=0, trim=False, scale='x2')
cls.res2 = results_pls.pls5
cls.rtol_fitted = 1e-5
# edf is currently not available with PenalizedMixin
# need correction for difference in scale denominator
cls.covp_corrfact = 1.0025464444310588
def setup_class(cls):
x0 = np.linspace(-0.2, 0.2, 11)
cls.params = np.column_stack((x0, x0))
cls.pen = smpen.L2ContraintsPenalty(restriction=[[1, 0], [1, 1]])
