Dev 114 cmdstan#801
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jeongyoonlee
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Jan 13, 2023
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| training_metrics = {"loglk": loglk} | ||
| training_metrics.update( | ||
| {"log_posterior": stan_mcmc_fit.get_logposterior(inc_warmup=True)} | ||
| ) | ||
| # log_posterior is not supported in cmdstanpy | ||
| # training_metrics.update( | ||
| # {"log_posterior": stan_mcmc_fit.get_logposterior(inc_warmup=True)} | ||
| # ) | ||
| training_metrics.update({"sampling_temperature": sampling_temperature}) |
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nit: we can create training_metrics at once instead of in two steps.
training_metrics = {
"loglk": loglk,
"sampling_temperature": sampling temperature,
}
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| training_metrics = dict() | ||
|
|
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| # extract `log_prob` in addition to defined model params | ||
| # this is for the BIC calculation | ||
| # loglk is needed for BIC calculation | ||
| training_metrics.update({"loglk": stan_extract["loglk"]}) | ||
| # FIXME: this needs to be the full length of all parameters instead of the one we sampled? | ||
| # FIXME: or it should be not include latent varaibles / derive variables? | ||
| # TODO: this needs to be the full length of all parameters instead of the one we sampled? | ||
| # TODO: or it should be not include latent variables / derive variables? | ||
| training_metrics.update({"num_of_params": len(model_param_names)}) |
Collaborator
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nit: same as above. Are there any reasons we're doing this way (i.e., creating a dict in multiple steps)?
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|
amazing! 🚀 ! |
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Description
A working branch to propose first solution in using cmdstanpy instead of pystan
Fixes #793
Type of change
PyStanHow Has This Been Tested?
All the original unit tests should be sufficient since this is a change just on the API. One small change is to add
loglkin the posterior keys in all types of estimators with Stan.