def convert_str_to_rv_dict(
model, start: StartDict
) -> Dict[TensorVariable, Optional[Union[np.ndarray, Variable, str]]]:
"""Helper function for converting a user-provided start dict with str keys of (transformed) variable names
to a dict mapping the RV tensors to untransformed initvals.
TODO: Deprecate this functionality and only accept TensorVariables as keys
"""
initvals = {}
for key, initval in start.items():
if isinstance(key, str):
if is_transformed_name(key):
rv = model[get_untransformed_name(key)]
initvals[rv] = model.rvs_to_values[rv].tag.transform.backward(
initval, *rv.owner.inputs)
else:
initvals[model[key]] = initval
else:
initvals[key] = initval
return initvals
def _run_convergence_checks(self, idata: arviz.InferenceData, model):
if not hasattr(idata, "posterior"):
msg = "No posterior samples. Unable to run convergence checks"
warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info", None,
None, None)
self._add_warnings([warn])
return
if idata.posterior.sizes["chain"] == 1:
msg = ("Only one chain was sampled, this makes it impossible to "
"run some convergence checks")
warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info")
self._add_warnings([warn])
return
elif idata.posterior.sizes["chain"] < 4:
msg = (
"We recommend running at least 4 chains for robust computation of "
"convergence diagnostics")
warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info")
self._add_warnings([warn])
return
valid_name = [rv.name for rv in model.free_RVs + model.deterministics]
varnames = []
for rv in model.free_RVs:
rv_name = rv.name
if is_transformed_name(rv_name):
rv_name2 = get_untransformed_name(rv_name)
rv_name = rv_name2 if rv_name2 in valid_name else rv_name
if rv_name in idata.posterior:
varnames.append(rv_name)
self._ess = ess = arviz.ess(idata, var_names=varnames)
self._rhat = rhat = arviz.rhat(idata, var_names=varnames)
warnings = []
rhat_max = max(val.max() for val in rhat.values())
if rhat_max > 1.01:
msg = ("The rhat statistic is larger than 1.01 for some "
"parameters. This indicates problems during sampling. "
"See https://arxiv.org/abs/1903.08008 for details")
warn = SamplerWarning(WarningType.CONVERGENCE,
msg,
"info",
extra=rhat)
warnings.append(warn)
eff_min = min(val.min() for val in ess.values())
eff_per_chain = eff_min / idata.posterior.sizes["chain"]
if eff_per_chain < 100:
msg = (
"The effective sample size per chain is smaller than 100 for some parameters. "
" A higher number is needed for reliable rhat and ess computation. "
"See https://arxiv.org/abs/1903.08008 for details")
warn = SamplerWarning(WarningType.CONVERGENCE,
msg,
"error",
extra=ess)
warnings.append(warn)
self._add_warnings(warnings)
def _run_convergence_checks(self, idata: arviz.InferenceData, model):
if not hasattr(idata, "posterior"):
msg = "No posterior samples. Unable to run convergence checks"
warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info", None,
None, None)
self._add_warnings([warn])
return
if idata.posterior.sizes["chain"] == 1:
msg = ("Only one chain was sampled, this makes it impossible to "
"run some convergence checks")
warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info")
self._add_warnings([warn])
return
valid_name = [rv.name for rv in model.free_RVs + model.deterministics]
varnames = []
for rv in model.free_RVs:
rv_name = rv.name
if is_transformed_name(rv_name):
rv_name2 = get_untransformed_name(rv_name)
rv_name = rv_name2 if rv_name2 in valid_name else rv_name
if rv_name in idata.posterior:
varnames.append(rv_name)
self._ess = ess = arviz.ess(idata, var_names=varnames)
self._rhat = rhat = arviz.rhat(idata, var_names=varnames)
warnings = []
rhat_max = max(val.max() for val in rhat.values())
if rhat_max > 1.4:
msg = ("The rhat statistic is larger than 1.4 for some "
"parameters. The sampler did not converge.")
warn = SamplerWarning(WarningType.CONVERGENCE,
msg,
"error",
extra=rhat)
warnings.append(warn)
elif rhat_max > 1.2:
msg = "The rhat statistic is larger than 1.2 for some " "parameters."
warn = SamplerWarning(WarningType.CONVERGENCE,
msg,
"warn",
extra=rhat)
warnings.append(warn)
elif rhat_max > 1.05:
msg = ("The rhat statistic is larger than 1.05 for some "
"parameters. This indicates slight problems during "
"sampling.")
warn = SamplerWarning(WarningType.CONVERGENCE,
msg,
"info",
extra=rhat)
warnings.append(warn)
eff_min = min(val.min() for val in ess.values())
sizes = idata.posterior.sizes
n_samples = sizes["chain"] * sizes["draw"]
if eff_min < 200 and n_samples >= 500:
msg = ("The estimated number of effective samples is smaller than "
"200 for some parameters.")
warn = SamplerWarning(WarningType.CONVERGENCE,
msg,
"error",
extra=ess)
warnings.append(warn)
elif eff_min / n_samples < 0.1:
msg = "The number of effective samples is smaller than " "10% for some parameters."
warn = SamplerWarning(WarningType.CONVERGENCE,
msg,
"warn",
extra=ess)
warnings.append(warn)
elif eff_min / n_samples < 0.25:
msg = "The number of effective samples is smaller than " "25% for some parameters."
warn = SamplerWarning(WarningType.CONVERGENCE,
msg,
"info",
extra=ess)
warnings.append(warn)
self._add_warnings(warnings)