def test_parameter_estimation_resampling(self):
X = RNG.uniform(0, 4, 1000)
y = X + RNG.normal(0, 1, 1000)
m = BayesianBootstrapBagging(LinearRegression(),
10000,
1000,
low_mem=False)
m.fit(X.reshape(-1, 1), y)
coef_samples = [b.coef_ for b in m.base_models_]
intercept_samples = [b.intercept_ for b in m.base_models_]
self.assertAlmostEqual(np.mean(coef_samples), 1, delta=0.3)
l, r = central_credible_interval(coef_samples, alpha=0.05)
self.assertLess(l, 1)
self.assertGreater(r, 1)
l, r = highest_density_interval(coef_samples, alpha=0.05)
self.assertLess(l, 1)
self.assertGreater(r, 1)
self.assertAlmostEqual(np.mean(intercept_samples), 0, delta=0.3)
l, r = central_credible_interval(intercept_samples, alpha=0.05)
self.assertLess(l, 0)
self.assertGreater(r, 0)
self.assertAlmostEqual(np.mean(intercept_samples), 0, delta=0.3)
l, r = highest_density_interval(intercept_samples, alpha=0.05)
self.assertLess(l, 0)
self.assertGreater(r, 0)
def test_central_credible_interval(self):
l, r = central_credible_interval(self._shuffle(range(10)), alpha=0.2)
self.assertEqual(l, 0.9)
self.assertEqual(r, 8.1)
l, r = central_credible_interval(self._shuffle(range(10)), alpha=0.19)
self.assertEqual(l, 0.855)
self.assertEqual(r, 8.145)
l, r = central_credible_interval(self._shuffle(range(20)), alpha=0.1)
self.assertAlmostEqual(l, 0.95)
self.assertEqual(r, 18.05)