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Fix max_treedepth warning #2808

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merged 2 commits into from
Jan 24, 2018
Merged

Fix max_treedepth warning #2808

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0193d7e
Fix max_treedepth warning
aseyboldt Jan 18, 2018
3d95880
Less sensitive neff warning
aseyboldt Jan 18, 2018
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8 changes: 7 additions & 1 deletion pymc3/backends/report.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,11 +102,17 @@ def _run_convergence_checks(self, trace):
warn = SamplerWarning(
WarningType.CONVERGENCE, msg, 'error', None, None, effective_n)
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', None, None, effective_n)
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, 'warn', None, None, effective_n)
WarningType.CONVERGENCE, msg, 'info', None, None, effective_n)
warnings.append(warn)

self._add_warnings(warnings)
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5 changes: 3 additions & 2 deletions pymc3/step_methods/hmc/base_hmc.py
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Expand Up @@ -177,8 +177,9 @@ def warnings(self, strace):
WarningType.DIVERGENCES, msg, 'error', None, None, None)
warnings.append(warning)
elif n_divs > 0:
message = ('Divergences after tuning. Increase `target_accept` or '
'reparameterize.')
message = ('There were %s divergences after tuning. Increase '
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Thank you for putting this back!

'`target_accept` or reparameterize.'
% n_divs)
warning = SamplerWarning(
WarningType.DIVERGENCES, message, 'error', None, None, None)
warnings.append(warning)
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7 changes: 5 additions & 2 deletions pymc3/step_methods/hmc/nuts.py
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Expand Up @@ -170,7 +170,8 @@ def _hamiltonian_step(self, start, p0, step_size):
if divergence_info or turning:
break
else:
self._reached_max_treedepth += 1
if not self.tune:
self._reached_max_treedepth += 1

stats = tree.stats()
accept_stat = stats['mean_tree_accept']
Expand All @@ -185,8 +186,10 @@ def competence(var, has_grad):

def warnings(self, strace):
warnings = super(NUTS, self).warnings(strace)
n_samples = self._samples_after_tune
n_treedepth = self._reached_max_treedepth

if np.mean(self._reached_max_treedepth) > 0.05:
if n_samples > 0 and n_treedepth / float(n_samples) > 0.05:
msg = ('The chain reached the maximum tree depth. Increase '
'max_treedepth, increase target_accept or reparameterize.')
warn = SamplerWarning(WarningType.TREEDEPTH, msg, 'warn',
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