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Better error message for NaN occurred in optimization #2990

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Merged
junpenglao merged 2 commits into from
May 29, 2018
Merged

Better error message for NaN occurred in optimization #2990

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cda0580
Better error message for NaN occurred in optimization
May 29, 2018
4da60f4
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May 29, 2018
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38 changes: 35 additions & 3 deletions pymc3/variational/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -147,8 +147,24 @@ def _iterate_without_loss(self, s, _, step_func, progress, callbacks):
try:
for i in progress:
step_func()
if np.isnan(self.approx.params[0].get_value()).any():
raise FloatingPointError('NaN occurred in optimization.')
current_param = self.approx.params[0].get_value()
if np.isnan(current_param).any():
name_slc = []
tmp_hold = list(range(current_param.size))
vmap = self.approx.groups[0].bij.ordering.vmap
for vmap_ in vmap:
slclen = len(tmp_hold[vmap_.slc])
for i in range(slclen):
name_slc.append((vmap_.var, i))
index = np.where(np.isnan(current_param))[0]
errmsg = ['NaN occurred in optimization. ']
suggest_solution = 'Try tracking this parameter: ' \
'http://docs.pymc.io/notebooks/variational_api_quickstart.html#Tracking-parameters'
for ii in index:
errmsg.append('The current approximation of RV `{}`.ravel()[{}]'
' is NaN.'.format(*name_slc[ii]))
errmsg.append(suggest_solution)
raise FloatingPointError('\n'.join(errmsg))
for callback in callbacks:
callback(self.approx, None, i+s+1)
except (KeyboardInterrupt, StopIteration) as e:
Expand Down Expand Up @@ -178,7 +194,23 @@ def _infmean(input_array):
if np.isnan(e): # pragma: no cover
scores = scores[:i]
self.hist = np.concatenate([self.hist, scores])
raise FloatingPointError('NaN occurred in optimization.')
current_param = self.approx.params[0].get_value()
name_slc = []
tmp_hold = list(range(current_param.size))
vmap = self.approx.groups[0].bij.ordering.vmap
for vmap_ in vmap:
slclen = len(tmp_hold[vmap_.slc])
for i in range(slclen):
name_slc.append((vmap_.var, i))
index = np.where(np.isnan(current_param))[0]
errmsg = ['NaN occurred in optimization. ']
suggest_solution = 'Try tracking this parameter: ' \
'http://docs.pymc.io/notebooks/variational_api_quickstart.html#Tracking-parameters'
for ii in index:
errmsg.append('The current approximation of RV `{}`.ravel()[{}]'
' is NaN.'.format(*name_slc[ii]))
errmsg.append(suggest_solution)
raise FloatingPointError('\n'.join(errmsg))
scores[i] = e
if i % 10 == 0:
avg_loss = _infmean(scores[max(0, i - 1000):i + 1])
Expand Down