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Add initial state tests #68

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Add initial state tests #68

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66fa47e
Test steady state approximation
Veganveins Mar 16, 2021
42d64ef
Test initial state probability estimation
Veganveins Mar 16, 2021
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62 changes: 62 additions & 0 deletions tests/test_estimation.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,62 @@
import numpy as np
import pymc3 as pm
import theano.tensor as tt

from pymc3_hmm.distributions import DiscreteMarkovChain, PoissonZeroProcess
from pymc3_hmm.step_methods import FFBSStep


def simulate_poiszero_hmm(
N,
observed,
mu=10.0,
pi_0_a=np.r_[1, 1],
p_0_a=np.r_[5, 1],
p_1_a=np.r_[1, 1],
pi_0=None,
):
p_0_rv = pm.Dirichlet("p_0", p_0_a)
p_1_rv = pm.Dirichlet("p_1", p_1_a)
P_tt = tt.stack([p_0_rv, p_1_rv])
P_rv = pm.Deterministic("P_tt", tt.shape_padleft(P_tt))
if pi_0 is not None:
pi_0_tt = tt.as_tensor(pi_0)
else:
pi_0_tt = pm.Dirichlet("pi_0", pi_0_a)
S_rv = DiscreteMarkovChain("S_t", P_rv, pi_0_tt, shape=N)
S_rv.tag.test_value = (observed > 0) * 1
return PoissonZeroProcess("Y_t", mu, S_rv, observed=observed)
Comment on lines +9 to +28
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As a simple test for initial probability estimation, I don't know if the zero-Poisson model is really the best choice.

If we're going to use a model with a Dirac emission distribution, we should use one that consists entirely of Dirac emissions; it's simpler and accomplishes the same thing.

Otherwise, we need to consider whether or not a non-Dirac model is a better choice for testing (e.g. mixture of normals). Likewise for the choice of transition matrix.



def test_gamma_0_estimation():

np.random.seed(2032)
true_initial_states = np.array([0.02, 0.98])

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seems like with true initial states set to [.02, .98] the initial state estimation fails. the error im seeing is

E       AssertionError: 
E       Arrays are not almost equal to 1 decimals
E       
E       Mismatched elements: 2 / 2 (100%)
E       Max absolute difference: 0.25904421
E       Max relative difference: 12.95221072
E        x: array([0.3, 0.7])
E        y: array([0., 1.])

with pm.Model(theano_config={"compute_test_value": "ignore"}) as sim_model:
_ = simulate_poiszero_hmm(30, np.zeros(30), 150, pi_0=true_initial_states)

sim_point = pm.sample_prior_predictive(samples=1, model=sim_model)
sim_point["Y_t"] = sim_point["Y_t"].squeeze()
y_test = sim_point["Y_t"]

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with pm.Model() as test_model:
_ = simulate_poiszero_hmm(
30,
y_test,
150,
)
states_step = FFBSStep([test_model["S_t"]])

posterior_trace = pm.sample(
step=[states_step],
draws=5,
return_inferencedata=True,
chains=1,
progressbar=True,
)

estimated_initial_state_probs = posterior_trace.posterior.pi_0.values[0].mean(0)
np.testing.assert_almost_equal(
estimated_initial_state_probs, true_initial_states, 1
)
15 changes: 15 additions & 0 deletions tests/test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import theano.tensor as tt

from pymc3_hmm.utils import (
compute_steady_state,
compute_trans_freqs,
logdotexp,
multilogit_inv,
Expand Down Expand Up @@ -120,3 +121,17 @@ def test_multilogit_inv(test_input, test_output):
res = multilogit_inv(tt.as_tensor_variable(test_input))
res = res.eval()
assert np.array_equal(res.round(2), test_output)


test_cases = [
(np.ones((2, 2)) * 0.5, np.array([0.5, 0.5])),
(np.eye(4), np.array([1, 0, 0, 0])),
]


@pytest.mark.parametrize("transition_matrix, steady_state", test_cases)
def test_compute_steady_state(transition_matrix, steady_state):

P = tt.as_tensor_variable(transition_matrix)
ss_probs = compute_steady_state(P)
np.testing.assert_almost_equal(ss_probs.eval(), steady_state, 1)
7 changes: 5 additions & 2 deletions tests/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@


def simulate_poiszero_hmm(
N, mu=10.0, pi_0_a=np.r_[1, 1], p_0_a=np.r_[5, 1], p_1_a=np.r_[1, 1]
N, mu=10.0, pi_0_a=np.r_[1, 1], p_0_a=np.r_[5, 1], p_1_a=np.r_[1, 1], pi_0=None
):

with pm.Model() as test_model:
Expand All @@ -16,7 +16,10 @@ def simulate_poiszero_hmm(
P_tt = tt.stack([p_0_rv, p_1_rv])
P_rv = pm.Deterministic("P_tt", tt.shape_padleft(P_tt))

pi_0_tt = pm.Dirichlet("pi_0", pi_0_a)
if pi_0:
pi_0_tt = pm.Dirichlet("pi_0", pi_0)
else:
pi_0_tt = pm.Dirichlet("pi_0", pi_0_a)

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@brandonwillard I think you helped me edit this simulate_poiszero_hmm function when I started working on this but I think I lost that change when I was trying to re-organize the commits. Does this if-else condition look right to you? I can't remember if this was exactly how we had it before

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@brandonwillard brandonwillard Mar 26, 2021 •
edited
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I don't recall; you'll need to check the Git history or walk through whatever problem(s) this is causing.

S_rv = DiscreteMarkovChain("S_t", P_rv, pi_0_tt, shape=N)

Expand Down