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Description
Describe the issue:
Trying to set up CategoricalGibbsMetropolis step method for a categorical variable with the proposal parameter set to 'proportional' causes an error KeyError: 'diverging' just as sampling starts.
Reproduceable code example:
import pymc as pm
with pm.Model() as model:
# Prior for categorical
p = pm.Dirichlet('p', a=[1., 1., 1.], shape=3)
# Categorical variable
category = pm.Categorical('category', p=p, shape=100)
# CategoricalGibbsMetropolis with proportional proposal
step = pm.CategoricalGibbsMetropolis(vars=[category], proposal = 'proportional')
trace = pm.sample(500, cores=1, chains=1, step=step, return_inferencedata=False)Error message:
Traceback (most recent call last):
File "h:\Fear_Generalization\import pymc as pm.py", line 14, in <module>
trace = pm.sample(500, cores=1, chains=1, step=step, return_inferencedata=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\fbabb\.conda\envs\pymc_env\Lib\site-packages\pymc\sampling\mcmc.py", line 929, in sample
_sample_many(**sample_args)
File "C:\Users\fbabb\.conda\envs\pymc_env\Lib\site-packages\pymc\sampling\mcmc.py", line 1072, in _sample_many
_sample(
File "C:\Users\fbabb\.conda\envs\pymc_env\Lib\site-packages\pymc\sampling\mcmc.py", line 1157, in _sample
for it, diverging in enumerate(sampling_gen):
^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\fbabb\.conda\envs\pymc_env\Lib\site-packages\pymc\sampling\mcmc.py", line 1224, in _iter_sample
trace.record(point, stats)
File "C:\Users\fbabb\.conda\envs\pymc_env\Lib\site-packages\pymc\backends\ndarray.py", line 116, in record
data[key][draw_idx] = val
~~~~^^^^^
KeyError: 'diverging'PyMC version information:
PyMC Version: 5.19.1
PyTensor Version: 2.26.3
Python Version: 3.12.7
Context for the issue:
Editing the pymc source code by copying this pattern at the end of astep_prop as suggested by Luciano Paz resolved the issue for me.