Install

First install CmdStanPy and CmdStan and make sure it works.

$ pip install cmdstancache

Usage

model = """
data {
  int N;
}
parameters {
  real<lower=-10.0, upper=10.0> x[N];
}
model {
  for (i in 1:N-1) {
         target += -2 * (100 * square(x[i+1] - square(x[i])) + square(1 - x[i]));
  }
}
"""
data = dict(N=2)

import cmdstancache

stan_variables, method_variables = cmdstancache.run_stan(
        model,
        data=data,
        # any other sample() parameters go here
        seed=42
)

Now comes the trick:

• If you run this code twice, the second time the stored result is read.

• If you add or modify a code comment, the same result is returned without having to rerun.

How it works

cmdstancache keeps a cache of code and data that has previously been used for MCMC sampling. If it already has the results, it returns it from the cache.

Here are the details:

1. The code is normalised (stripped of comments and indents)

2. A hash of the normalised code is computed

3. The model code is stored in ~/.stan_cache/<codehash>.stan

4. The model is compiled, if it is not already there

5. The data are sorted by key, exported to json, and a hash computed

6. The data are stored in ~/.stan_cache/<datahash>.json

7. cmdstanpy MCMC is run with code=<codehash>.stan and data=<datahash>.json

8. fit.stan_variables() and fit.method_variables() are returned

9. joblib memoizes steps 7 and 8, avoiding resampling when the same data and code hash are seen.

Plotting

Make a quick corner plots of only the scalar model variables:

cmdstancache.plot_corner(stan_variables)

In case some chains are stuck, and you want to remove their samples for plotting:

cleaned_variables = remove_stuck_chains(stan_variables, method_variables)
plot = plot_corner(cleaned_variables)

Since this is optional, the dependency of corner is pulled in if installed with:

$ pip install cmdstancache[plot]

Contributors

• @JohannesBuchner

Contributions are welcome.