def test_FFBSStep(): np.random.seed(2032) poiszero_sim, _ = simulate_poiszero_hmm(30, 150) y_test = poiszero_sim["Y_t"] with pm.Model() as test_model: p_0_rv = pm.Dirichlet("p_0", np.r_[1, 1]) p_1_rv = pm.Dirichlet("p_1", np.r_[1, 1]) P_tt = tt.stack([p_0_rv, p_1_rv]) P_rv = pm.Deterministic("P_tt", tt.shape_padleft(P_tt)) pi_0_tt = compute_steady_state(P_rv) S_rv = HMMStateSeq("S_t", P_rv, pi_0_tt, shape=y_test.shape[0]) Y_rv = PoissonZeroProcess("Y_t", 9.0, S_rv, observed=y_test) with test_model: ffbs = FFBSStep([S_rv]) test_point = test_model.test_point.copy() test_point["p_0_stickbreaking__"] = poiszero_sim["p_0_stickbreaking__"] test_point["p_1_stickbreaking__"] = poiszero_sim["p_1_stickbreaking__"] res = ffbs.step(test_point) assert np.array_equal(res["S_t"], poiszero_sim["S_t"])
def test_FFBSStep(): with pm.Model(), pytest.raises(ValueError): P_rv = np.eye(2)[None, ...] S_rv = DiscreteMarkovChain("S_t", P_rv, np.r_[1.0, 0.0], shape=10) S_2_rv = DiscreteMarkovChain("S_2_t", P_rv, np.r_[0.0, 1.0], shape=10) PoissonZeroProcess("Y_t", 9.0, S_rv + S_2_rv, observed=np.random.poisson(9.0, size=10)) # Only one variable can be sampled by this step method ffbs = FFBSStep([S_rv, S_2_rv]) with pm.Model(), pytest.raises(TypeError): S_rv = pm.Categorical("S_t", np.r_[1.0, 0.0], shape=10) PoissonZeroProcess("Y_t", 9.0, S_rv, observed=np.random.poisson(9.0, size=10)) # Only `DiscreteMarkovChains` can be sampled with this step method ffbs = FFBSStep([S_rv]) with pm.Model(), pytest.raises(TypeError): P_rv = np.eye(2)[None, ...] S_rv = DiscreteMarkovChain("S_t", P_rv, np.r_[1.0, 0.0], shape=10) pm.Poisson("Y_t", S_rv, observed=np.random.poisson(9.0, size=10)) # Only `SwitchingProcess`es can used as dependent variables ffbs = FFBSStep([S_rv]) np.random.seed(2032) poiszero_sim, _ = simulate_poiszero_hmm(30, 150) y_test = poiszero_sim["Y_t"] with pm.Model() as test_model: p_0_rv = pm.Dirichlet("p_0", np.r_[1, 1], shape=2) p_1_rv = pm.Dirichlet("p_1", np.r_[1, 1], shape=2) P_tt = at.stack([p_0_rv, p_1_rv]) P_rv = pm.Deterministic("P_tt", at.shape_padleft(P_tt)) pi_0_tt = compute_steady_state(P_rv) S_rv = DiscreteMarkovChain("S_t", P_rv, pi_0_tt, shape=y_test.shape[0]) PoissonZeroProcess("Y_t", 9.0, S_rv, observed=y_test) with test_model: ffbs = FFBSStep([S_rv]) test_point = test_model.test_point.copy() test_point["p_0_stickbreaking__"] = poiszero_sim["p_0_stickbreaking__"] test_point["p_1_stickbreaking__"] = poiszero_sim["p_1_stickbreaking__"] res = ffbs.step(test_point) assert np.array_equal(res["S_t"], poiszero_sim["S_t"])
def test_TransMatConjugateStep(): with pm.Model() as test_model, pytest.raises(ValueError): p_0_rv = pm.Dirichlet("p_0", np.r_[1, 1], shape=2) transmat = TransMatConjugateStep(p_0_rv) np.random.seed(2032) poiszero_sim, _ = simulate_poiszero_hmm(30, 150) y_test = poiszero_sim["Y_t"] with pm.Model() as test_model: p_0_rv = pm.Dirichlet("p_0", np.r_[1, 1], shape=2) p_1_rv = pm.Dirichlet("p_1", np.r_[1, 1], shape=2) P_tt = at.stack([p_0_rv, p_1_rv]) P_rv = pm.Deterministic("P_tt", at.shape_padleft(P_tt)) pi_0_tt = compute_steady_state(P_rv) S_rv = DiscreteMarkovChain("S_t", P_rv, pi_0_tt, shape=y_test.shape[0]) PoissonZeroProcess("Y_t", 9.0, S_rv, observed=y_test) with test_model: transmat = TransMatConjugateStep(P_rv) test_point = test_model.test_point.copy() test_point["S_t"] = (y_test > 0).astype(int) res = transmat.step(test_point) p_0_smpl = get_test_value( p_0_rv.distribution.transform.backward(res[p_0_rv.transformed.name])) p_1_smpl = get_test_value( p_1_rv.distribution.transform.backward(res[p_1_rv.transformed.name])) sampled_trans_mat = np.stack([p_0_smpl, p_1_smpl]) true_trans_mat = ( compute_trans_freqs(poiszero_sim["S_t"], 2, counts_only=True) + np.c_[[1, 1], [1, 1]]) true_trans_mat = true_trans_mat / true_trans_mat.sum(0)[..., None] assert np.allclose(sampled_trans_mat, true_trans_mat, atol=0.3)