def test_draw_coefficients(self):
# Make the coefficients big, so that effects are obvious.
unscaled_innovation_sd = np.array([10, 20, 30])
data, coefficients, inclusion = self.simulate_data_from_model(
time_dimension=100,
typical_sample_size=500,
xdim=self._xdim,
residual_sd=self._residual_sd,
unscaled_innovation_sd=unscaled_innovation_sd,
p00=self._p00,
p11=self._p11)
model, _ = self.setup_model(data, coefficients, inclusion,
self._residual_sd, unscaled_innovation_sd,
self._p00, self._p11)
niter = 1000
draws = np.full((niter, model.xdim, model.time_dimension), np.NaN)
for i in range(niter):
model.draw_coefficients_given_inclusion(boom.GlobalRng.rng)
draws[i, :, :] = model.all_coefficients.to_numpy()
posterior_mean = np.mean(draws, axis=0)
mean_vector = posterior_mean.flatten()
beta_vector = coefficients.flatten()
cor = R.corr(mean_vector, beta_vector)
self.assertGreater(cor, .9)
def test_draw_inclusion_indicators(self):
"""
Check that the model draws the inclusion indicators conditional on all
other unknowns fixed at their true values. The regression coefficients
are integrated out and not conditioned on.
"""
# Make the coefficients big, so that effects are obvious.
unscaled_innovation_sd = np.array([10, 20, 30])
data, coefficients, inclusion = self.simulate_data_from_model(
time_dimension=100,
typical_sample_size=500,
xdim=self._xdim,
residual_sd=self._residual_sd,
unscaled_innovation_sd=unscaled_innovation_sd,
p00=self._p00,
p11=self._p11)
model, sampler = self.setup_model(data, coefficients, inclusion,
self._residual_sd,
unscaled_innovation_sd, self._p00,
self._p11)
niter = 1000
draws = np.full((niter, model.xdim, model.time_dimension), -1)
for i in range(niter):
sampler.draw_inclusion_indicators()
draws[i, :, :] = model.inclusion_indicators.to_numpy()
posterior_mean = np.mean(draws[100:, :], axis=0)
mean_vector = posterior_mean.flatten()
inclusion_vector = inclusion.flatten()
cor = R.corr(inclusion_vector, mean_vector)
self.assertGreater(cor, .6)
