def test_inference_data_attrs(self, posterior, prior, save_warmup, warmup_iterations: int):
arviz_inference_data_from_pyjags_samples_dict = from_pyjags(
posterior=posterior,
prior=prior,
log_likelihood={"y": "log_like"},
save_warmup=save_warmup,
warmup_iterations=warmup_iterations,
)
posterior_warmup_prefix = (
"" if save_warmup and warmup_iterations > 0 and posterior is not None else "~"
)
prior_warmup_prefix = (
"" if save_warmup and warmup_iterations > 0 and prior is not None else "~"
)
print(f'posterior_warmup_prefix="{posterior_warmup_prefix}"')
test_dict = {
f'{"~" if posterior is None else ""}posterior': ["b", "int"],
f'{"~" if prior is None else ""}prior': ["b", "int"],
f'{"~" if posterior is None else ""}log_likelihood': ["y"],
f"{posterior_warmup_prefix}warmup_posterior": ["b", "int"],
f"{prior_warmup_prefix}warmup_prior": ["b", "int"],
f"{posterior_warmup_prefix}warmup_log_likelihood": ["y"],
}
fails = check_multiple_attrs(test_dict, arviz_inference_data_from_pyjags_samples_dict)
assert not fails
def test_roundtrip_from_pyjags_via_arviz_to_pyjags(self):
arviz_inference_data_from_pyjags_samples_dict = from_pyjags(
PYJAGS_POSTERIOR_DICT)
arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data(
arviz_inference_data_from_pyjags_samples_dict)
pyjags_dict_from_arviz_idata = _convert_arviz_dict_to_pyjags_dict(
arviz_dict_from_idata_from_pyjags_dict)
assert verify_equality_of_numpy_values_dictionaries(
PYJAGS_POSTERIOR_DICT, pyjags_dict_from_arviz_idata)
def __call__(self, samples: tp.Dict[str, np.ndarray],
verbose: bool) -> bool:
idata = az.from_pyjags(samples)
ess = az.ess(idata, var_names=self.variable_names)
minimum_ess = min(value['data']
for key, value in ess.to_dict()['data_vars'].items())
if verbose:
print(f'minimum ess = {minimum_ess}')
return minimum_ess >= self.minimum_ess
def __call__(self, samples: tp.Dict[str, np.ndarray],
verbose: bool) -> bool:
idata = az.from_pyjags(samples)
rhat = az.rhat(idata, var_names=self.variable_names)
maximum_rhat_deviation = max(
abs(value['data'] - 1.0)
for key, value in rhat.to_dict()['data_vars'].items())
if verbose:
print(f'maximum rhat deviation = {maximum_rhat_deviation}')
return maximum_rhat_deviation <= self.maximum_rhat_deviation
def test_extract_samples_dictionary_from_arviz_inference_data(self):
arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict(
PYJAGS_POSTERIOR_DICT)
arviz_inference_data_from_pyjags_samples_dict = from_pyjags(
PYJAGS_POSTERIOR_DICT)
arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data(
arviz_inference_data_from_pyjags_samples_dict)
assert verify_equality_of_numpy_values_dictionaries(
arviz_samples_dict_from_pyjags_samples_dict,
arviz_dict_from_idata_from_pyjags_dict,
)
