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test_distributions.py
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test_distributions.py
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import numpy as np
import pymc3 as pm
import theano.tensor as tt
from tests.utils import simulate_poiszero_hmm
from pymc3_hmm.distributions import PoissonZeroProcess, HMMStateSeq, SwitchingProcess
def test_HMMStateSeq_random():
test_gamma_0 = np.r_[0.0, 1.0]
test_Gamma = np.stack([[1.0, 0.0], [0.0, 1.0]])
assert np.all(HMMStateSeq.dist(10, test_Gamma, test_gamma_0).random() == 1)
assert np.all(HMMStateSeq.dist(10, test_Gamma, 1.0 - test_gamma_0).random() == 0)
assert HMMStateSeq.dist(10, test_Gamma, test_gamma_0).random(size=12).shape == (
12,
10,
)
test_sample = HMMStateSeq.dist(10, test_Gamma, test_gamma_0).random(size=2)
assert np.array_equal(
test_sample, np.stack([np.ones(10), np.ones(10)], 0).astype(int)
)
# TODO: Fix the seed, and make sure there's at least one 0 and 1
test_gamma_0 = np.r_[0.2, 0.8]
test_Gamma = np.stack([[0.8, 0.2], [0.2, 0.8]])
test_sample = HMMStateSeq.dist(10, test_Gamma, test_gamma_0).random(size=2)
# test_sample
assert test_sample.shape == (2, 10)
test_gamma_0 = np.stack([np.r_[0.0, 1.0], np.r_[1.0, 0.0]])
test_Gamma = np.stack(
[np.stack([[1.0, 0.0], [0.0, 1.0]]), np.stack([[1.0, 0.0], [0.0, 1.0]])]
)
test_sample = HMMStateSeq.dist(10, test_Gamma, test_gamma_0).random()
# test_sample
assert np.array_equal(
test_sample, np.stack([np.ones(10), np.zeros(10)], 0).astype(int)
)
assert test_sample.shape == (2, 10)
def test_HMMStateSeq_point():
test_Gamma = tt.as_tensor_variable(np.stack([[1.0, 0.0], [0.0, 1.0]]))
with pm.Model():
# XXX: `draw_values` won't use the `Deterministic`s values in the `point` map!
# Also, `Constant` is only for integer types (?!), so we can't use that.
test_gamma_0 = pm.Dirichlet("gamma_0", np.r_[1.0, 1000.0])
test_point = {"gamma_0": np.r_[1.0, 0.0]}
assert np.all(
HMMStateSeq.dist(10, test_Gamma, test_gamma_0).random(point=test_point) == 0
)
assert np.all(
HMMStateSeq.dist(10, test_Gamma, 1.0 - test_gamma_0).random(
point=test_point
)
== 1
)
def test_PoissonZeroProcess_random():
test_states = np.r_[0, 0, 1, 1, 0, 1]
test_dist = PoissonZeroProcess.dist(10.0, test_states)
assert np.array_equal(test_dist.shape, test_states.shape)
test_sample = test_dist.random()
assert test_sample.shape == (test_states.shape[0],)
assert np.all(test_sample[test_states > 0] > 0)
test_sample = test_dist.random(size=5)
assert np.array_equal(test_sample.shape, (5,) + test_states.shape)
assert np.all(test_sample[..., test_states > 0] > 0)
test_states = np.r_[0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0]
test_dist = PoissonZeroProcess.dist(100.0, test_states)
assert np.array_equal(test_dist.shape, test_states.shape)
test_sample = test_dist.random(size=1)
assert np.array_equal(test_sample.shape, (1,) + test_states.shape)
assert np.all(test_sample[..., test_states > 0] > 0)
test_states = np.r_[0, 0, 1, 1, 0, 1]
test_mus = np.r_[10.0, 10.0, 10.0, 20.0, 20.0, 20.0]
test_dist = PoissonZeroProcess.dist(test_mus, test_states)
assert np.array_equal(test_dist.shape, test_states.shape)
test_sample = test_dist.random()
assert np.array_equal(test_sample.shape, test_states.shape)
assert np.all(test_sample[..., test_states > 0] > 0)
test_states = np.c_[0, 0, 1, 1, 0, 1].T
test_dist = PoissonZeroProcess.dist(test_mus, test_states)
assert np.array_equal(test_dist.shape, test_states.shape)
test_sample = test_dist.random()
# TODO: This seems bad, but also what PyMC3 would do
assert np.array_equal(test_sample.shape, test_states.squeeze().shape)
assert np.all(test_sample[..., test_states.squeeze() > 0] > 0)
test_states = np.r_[0, 0, 1, 1, 0, 1]
test_sample = PoissonZeroProcess.dist(10.0, test_states).random(size=3)
assert np.array_equal(test_sample.shape, (3,) + test_states.shape)
assert np.all(test_sample.sum(0)[..., test_states > 0] > 0)
def test_PoissonZeroProcess_point():
test_states = np.r_[0, 0, 1, 1, 0, 1]
with pm.Model():
test_mean = pm.Constant("c", 1000.0)
test_point = {"c": 100.0}
test_sample = PoissonZeroProcess.dist(test_mean, test_states).random(
point=test_point
)
assert np.all(0 < test_sample[..., test_states > 0])
assert np.all(test_sample[..., test_states > 0] < 200)
def test_random_PoissonZeroProcess_HMMStateSeq():
poiszero_sim, test_model = simulate_poiszero_hmm(30, 5000)
y_test = poiszero_sim["Y_t"].squeeze()
nonzeros_idx = poiszero_sim["S_t"] > 0
assert np.all(y_test[nonzeros_idx] > 0)
assert np.all(y_test[~nonzeros_idx] == 0)
def test_SwitchingProcess():
np.random.seed(2023532)
test_states = np.r_[2, 0, 1, 2, 0, 1]
test_dists = [pm.Constant.dist(0), pm.Poisson.dist(100.0), pm.Poisson.dist(1000.0)]
test_dist = SwitchingProcess.dist(test_dists, test_states)
assert np.array_equal(test_dist.shape, test_states.shape)
test_sample = test_dist.random()
assert test_sample.shape == (test_states.shape[0],)
assert np.all(test_sample[test_states == 0] == 0)
assert np.all(0 < test_sample[test_states == 1])
assert np.all(test_sample[test_states == 1] < 1000)
assert np.all(100 < test_sample[test_states == 2])
test_mus = np.r_[100, 100, 500, 100, 100, 100]
test_dists = [
pm.Constant.dist(0),
pm.Poisson.dist(test_mus),
pm.Poisson.dist(10000.0),
]
test_dist = SwitchingProcess.dist(test_dists, test_states)
assert np.array_equal(test_dist.shape, test_states.shape)
test_sample = test_dist.random()
assert test_sample.shape == (test_states.shape[0],)
assert np.all(200 < test_sample[2] < 600)
assert np.all(0 < test_sample[5] < 200)
assert np.all(5000 < test_sample[test_states == 2])
test_dists = [pm.Constant.dist(0), pm.Poisson.dist(100.0), pm.Poisson.dist(1000.0)]
test_dist = SwitchingProcess.dist(test_dists, test_states)
for i in range(len(test_dists)):
test_logp = test_dist.logp(
np.tile(test_dists[i].mode.eval(), test_states.shape)
).eval()
assert test_logp[test_states != i].max() < test_logp[test_states == i].min()
# Try a continuous mixture
test_states = np.r_[2, 0, 1, 2, 0, 1]
test_dists = [
pm.Normal.dist(0.0, 1.0),
pm.Normal.dist(100.0, 1.0),
pm.Normal.dist(1000.0, 1.0),
]
test_dist = SwitchingProcess.dist(test_dists, test_states)
assert np.array_equal(test_dist.shape, test_states.shape)
test_sample = test_dist.random()
assert test_sample.shape == (test_states.shape[0],)
assert np.all(test_sample[test_states == 0] < 10)
assert np.all(50 < test_sample[test_states == 1])
assert np.all(test_sample[test_states == 1] < 150)
assert np.all(900 < test_sample[test_states == 2])
def test_subset_args():
test_dist = pm.NegativeBinomial.dist(mu=np.r_[0.1, 1.2, 2.3], alpha=2)
res = test_dist.subset_args(shape=[3], idx=np.r_[0, 2])
assert np.array_equal(res[0].eval(), np.r_[0.1, 2.3])
assert np.array_equal(res[1].eval(), np.r_[2.0, 2.0])