def test_sample_posterior_predictive_w(self):
data0 = np.random.normal(0, 1, size=50)
warning_msg = "The number of samples is too small to check convergence reliably"
with pm.Model() as model_0:
mu = pm.Normal("mu", mu=0, sigma=1)
y = pm.Normal("y", mu=mu, sigma=1, observed=data0)
with pytest.warns(UserWarning, match=warning_msg):
trace_0 = pm.sample(10, tune=0, chains=2, return_inferencedata=False)
idata_0 = pm.to_inference_data(trace_0, log_likelihood=False)
with pm.Model() as model_1:
mu = pm.Normal("mu", mu=0, sigma=1, size=len(data0))
y = pm.Normal("y", mu=mu, sigma=1, observed=data0)
with pytest.warns(UserWarning, match=warning_msg):
trace_1 = pm.sample(10, tune=0, chains=2, return_inferencedata=False)
idata_1 = pm.to_inference_data(trace_1, log_likelihood=False)
with pm.Model() as model_2:
# Model with no observed RVs.
mu = pm.Normal("mu", mu=0, sigma=1)
with pytest.warns(UserWarning, match=warning_msg):
trace_2 = pm.sample(10, tune=0, return_inferencedata=False)
traces = [trace_0, trace_1]
idatas = [idata_0, idata_1]
models = [model_0, model_1]
ppc = pm.sample_posterior_predictive_w(traces, 100, models)
assert ppc["y"].shape == (100, 50)
ppc = pm.sample_posterior_predictive_w(idatas, 100, models)
assert ppc["y"].shape == (100, 50)
with model_0:
ppc = pm.sample_posterior_predictive_w([idata_0.posterior], None)
assert ppc["y"].shape == (20, 50)
with pytest.raises(ValueError, match="The number of traces and weights should be the same"):
pm.sample_posterior_predictive_w([idata_0.posterior], 100, models, weights=[0.5, 0.5])
with pytest.raises(ValueError, match="The number of models and weights should be the same"):
pm.sample_posterior_predictive_w([idata_0.posterior], 100, models)
with pytest.raises(
ValueError, match="The number of observed RVs should be the same for all models"
):
pm.sample_posterior_predictive_w([trace_0, trace_2], 100, [model_0, model_2])
def test_sample_from_xarray_prior(self, point_list_arg_bug_fixture):
pmodel, trace = point_list_arg_bug_fixture
with pmodel:
prior = pm.sample_prior_predictive(
samples=20,
return_inferencedata=False,
)
idat = pm.to_inference_data(trace, prior=prior)
with pmodel:
pp = pm.sample_posterior_predictive(
idat.prior, return_inferencedata=False, var_names=["d"]
)
def test_normal_vector_idata(self, caplog):
with pm.Model() as model:
mu = pm.Normal("mu", 0.0, 1.0)
a = pm.Normal("a", mu=mu, sigma=1, observed=np.array([0.5, 0.2]))
trace = pm.sample(return_inferencedata=False)
assert not isinstance(trace, InferenceData)
with model:
# test keep_size parameter with inference data as input...
idata = pm.to_inference_data(trace)
assert isinstance(idata, InferenceData)
ppc = pm.sample_posterior_predictive(idata, return_inferencedata=False, keep_size=True)
assert ppc["a"].shape == (trace.nchains, len(trace), 2)
def test_normal_scalar_idata(self):
nchains = 2
ndraws = 500
with pm.Model() as model:
mu = pm.Normal("mu", 0.0, 1.0)
a = pm.Normal("a", mu=mu, sigma=1, observed=0.0)
trace = pm.sample(
draws=ndraws,
chains=nchains,
return_inferencedata=False,
discard_tuned_samples=False,
)
assert not isinstance(trace, InferenceData)
with model:
# test keep_size parameter and idata input
idata = pm.to_inference_data(trace)
assert isinstance(idata, InferenceData)
ppc = pm.sample_posterior_predictive(idata, keep_size=True, return_inferencedata=False)
assert ppc["a"].shape == (nchains, ndraws)
def test_sample_from_xarray_posterior(self, point_list_arg_bug_fixture):
pmodel, trace = point_list_arg_bug_fixture
with pmodel:
idat = pm.to_inference_data(trace)
pp = pm.sample_posterior_predictive(idat.posterior, var_names=["d"])