I’m not sure if this is worth reporting as a very minor error, but I recently fit a rather straightforward model (1 variable sampled, <500k data points, sampling done in under 10 minutes, with no warnings reported at all), and
pm.traceplot(my_difficult_trace) simply does not work for its trace. It ends up taking up all the RAM in my system and crashing my kernel, as well as lagging my computer to a complete standstill, all within a few minutes.
The first issue is that the trace may be buggy in some sense, but I reran the model with no errors, and the new trace displayed the same behavior, not allowing
pm.traceplot() to work. The second issue is how problematic it is that a difficult plot would be enough to freeze my computer within a few minutes if it was proving the slightest bit difficult, but I guess that’s a matplotlib error
Would someone examine the trace I am uploading, and see if they experience a similar error? Thank you all for your attention on this matter.
Link to the trace (mega seemed the most efficient way to share it) is here.
with pm.Model() as model: # Prior # The upper bound is the range of my dataset, multiplied by 5 to give a naive but simple diffuse prior sigma = pm.Uniform('sigma', lower=0, upper = 5 * (max(np.hstack(my_data)) - min(np.hstack(my_data))) ) # Likelihood estimate = pm.Normal('estimate', mu = np.mean( np.hstack(my_data) ), sigma = sigma, observed = np.hstack(my_data)) trace = pm.sample(cores=4, n_init=20000, draws=2000, tune=8000)