def test_float64(self):
with Model() as model:
x = Normal("x", initval=np.array(1.0, dtype="float64"))
obs = Normal("obs", mu=x, sigma=1.0, observed=np.random.randn(5))
assert x.dtype == "float64"
assert obs.dtype == "float64"
for sampler in self.samplers:
with model:
sample(draws=10, tune=10, chains=1, step=sampler())
def test_list_normals_sampling(self):
norm_w = np.array([0.75, 0.25])
norm_mu = np.array([0.0, 5.0])
norm_sigma = np.ones_like(norm_mu)
norm_x = generate_normal_mixture_data(norm_w, norm_mu, norm_sigma, size=1000)
with Model() as model:
w = Dirichlet("w", floatX(np.ones_like(norm_w)), shape=norm_w.size)
mu = Normal("mu", 0.0, 10.0, shape=norm_w.size)
tau = Gamma("tau", 1.0, 1.0, shape=norm_w.size)
Mixture(
"x_obs",
w,
[Normal.dist(mu[0], tau=tau[0]), Normal.dist(mu[1], tau=tau[1])],
observed=norm_x,
)
trace = sample(
5000,
chains=1,
step=Metropolis(),
random_seed=self.random_seed,
progressbar=False,
return_inferencedata=False,
)
assert_allclose(np.sort(trace["w"].mean(axis=0)), np.sort(norm_w), rtol=0.1, atol=0.1)
assert_allclose(np.sort(trace["mu"].mean(axis=0)), np.sort(norm_mu), rtol=0.1, atol=0.1)
def test_float32_MLDA(self):
data = np.random.randn(5).astype("float32")
with Model() as coarse_model:
x = Normal("x", initval=np.array(1.0, dtype="float32"))
obs = Normal("obs", mu=x, sigma=1.0, observed=data + 0.5)
with Model() as model:
x = Normal("x", initval=np.array(1.0, dtype="float32"))
obs = Normal("obs", mu=x, sigma=1.0, observed=data)
assert x.dtype == "float32"
assert obs.dtype == "float32"
with model:
sample(draws=10,
tune=10,
chains=1,
step=MLDA(coarse_models=[coarse_model]))
def test_interval_missing_observations():
with Model() as model:
obs1 = ma.masked_values([1, 2, -1, 4, -1], value=-1)
obs2 = ma.masked_values([-1, -1, 6, -1, 8], value=-1)
rng = aesara.shared(np.random.RandomState(2323), borrow=True)
with pytest.warns(ImputationWarning):
theta1 = Uniform("theta1", 0, 5, observed=obs1, rng=rng)
with pytest.warns(ImputationWarning):
theta2 = Normal("theta2", mu=theta1, observed=obs2, rng=rng)
assert "theta1_observed" in model.named_vars
assert "theta1_missing_interval__" in model.named_vars
assert not hasattr(
model.rvs_to_values[model.named_vars["theta1_observed"]].tag, "transform"
)
prior_trace = sample_prior_predictive(return_inferencedata=False)
# Make sure the observed + missing combined deterministics have the
# same shape as the original observations vectors
assert prior_trace["theta1"].shape[-1] == obs1.shape[0]
assert prior_trace["theta2"].shape[-1] == obs2.shape[0]
# Make sure that the observed values are newly generated samples
assert np.all(np.var(prior_trace["theta1_observed"], 0) > 0.0)
assert np.all(np.var(prior_trace["theta2_observed"], 0) > 0.0)
# Make sure the missing parts of the combined deterministic matches the
# sampled missing and observed variable values
assert np.mean(prior_trace["theta1"][:, obs1.mask] - prior_trace["theta1_missing"]) == 0.0
assert np.mean(prior_trace["theta1"][:, ~obs1.mask] - prior_trace["theta1_observed"]) == 0.0
assert np.mean(prior_trace["theta2"][:, obs2.mask] - prior_trace["theta2_missing"]) == 0.0
assert np.mean(prior_trace["theta2"][:, ~obs2.mask] - prior_trace["theta2_observed"]) == 0.0
assert {"theta1", "theta2"} <= set(prior_trace.keys())
trace = sample(
chains=1, draws=50, compute_convergence_checks=False, return_inferencedata=False
)
assert np.all(0 < trace["theta1_missing"].mean(0))
assert np.all(0 < trace["theta2_missing"].mean(0))
assert "theta1" not in trace.varnames
assert "theta2" not in trace.varnames
# Make sure that the observed values are newly generated samples and that
# the observed and deterministic matche
pp_trace = sample_posterior_predictive(trace, return_inferencedata=False, keep_size=False)
assert np.all(np.var(pp_trace["theta1"], 0) > 0.0)
assert np.all(np.var(pp_trace["theta2"], 0) > 0.0)
assert np.mean(pp_trace["theta1"][:, ~obs1.mask] - pp_trace["theta1_observed"]) == 0.0
assert np.mean(pp_trace["theta2"][:, ~obs2.mask] - pp_trace["theta2_observed"]) == 0.0
def test_missing_dual_observations():
with Model() as model:
obs1 = ma.masked_values([1, 2, -1, 4, -1], value=-1)
obs2 = ma.masked_values([-1, -1, 6, -1, 8], value=-1)
beta1 = Normal("beta1", 1, 1)
beta2 = Normal("beta2", 2, 1)
latent = Normal("theta", size=5)
with pytest.warns(ImputationWarning):
ovar1 = Normal("o1", mu=beta1 * latent, observed=obs1)
with pytest.warns(ImputationWarning):
ovar2 = Normal("o2", mu=beta2 * latent, observed=obs2)
prior_trace = sample_prior_predictive(return_inferencedata=False)
assert {"beta1", "beta2", "theta", "o1", "o2"} <= set(prior_trace.keys())
# TODO: Assert something
trace = sample(chains=1, draws=50)
def test_list_poissons_sampling(self):
pois_w = np.array([0.4, 0.6])
pois_mu = np.array([5.0, 20.0])
pois_x = generate_poisson_mixture_data(pois_w, pois_mu, size=1000)
with Model() as model:
w = Dirichlet("w", floatX(np.ones_like(pois_w)), shape=pois_w.shape)
mu = Gamma("mu", 1.0, 1.0, shape=pois_w.size)
Mixture("x_obs", w, [Poisson.dist(mu[0]), Poisson.dist(mu[1])], observed=pois_x)
trace = sample(
5000,
chains=1,
step=Metropolis(),
random_seed=self.random_seed,
progressbar=False,
return_inferencedata=False,
)
assert_allclose(np.sort(trace["w"].mean(axis=0)), np.sort(pois_w), rtol=0.1, atol=0.1)
assert_allclose(np.sort(trace["mu"].mean(axis=0)), np.sort(pois_mu), rtol=0.1, atol=0.1)
def test_linear():
lam = -0.78
sig2 = 5e-3
N = 300
dt = 1e-1
sde = lambda x, lam: (lam * x, sig2)
x = floatX(_gen_sde_path(sde, (lam, ), dt, N, 5.0))
z = x + np.random.randn(x.size) * sig2
# build model
with Model() as model:
lamh = Flat("lamh")
xh = EulerMaruyama("xh", dt, sde, (lamh, ), shape=N + 1, initval=x)
Normal("zh", mu=xh, sigma=sig2, observed=z)
# invert
with model:
trace = sample(init="advi+adapt_diag", chains=1)
ppc = sample_posterior_predictive(trace, model=model)
p95 = [2.5, 97.5]
lo, hi = np.percentile(trace[lamh], p95, axis=0)
assert (lo < lam) and (lam < hi)
lo, hi = np.percentile(ppc["zh"], p95, axis=0)
assert ((lo < z) * (z < hi)).mean() > 0.95
