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pystanpy

PyStanPy lets you define your model in Python with whatever technique you like for computing lp and its gradient.

Why

Iterating on models is easier when you don't need to wait. Or, you've got a model accessible in Python already. Etc etc.

Details

This works by building a CmdStan model with a user defined function which invokes a Python callable lp and glp in the model module (however determined by sys.path or $PYTHONPATH), as for example in this repo, where the model,

from autograd import grad, numpy as np

def lp(x):
    err = np.r_[:x.size] - x
    err = err * 5
    return np.sum(-err**2)

glp = grad(lp)

and the cmdstanpy invocation,

if __name__ == '__main__':
    from cmdstanpy import CmdStanModel
    model = CmdStanModel(exe_file='main')
    fit = model.sample(data={'n': 5}, chains=1, iter_sampling=200, iter_warmup=200)
    draws = fit.draws()
    print('draws.shape', draws.shape)
    print('inferred shifts (should be r_[:5])',
          np.round(draws.mean(axis=0)[0][-5:]))

are in the same file for convenience. When run, we get the expected result,

~/src/pystanpy> PYTHONPATH=$PWD python3 model.py
INFO:cmdstanpy:start chain 1
INFO:cmdstanpy:finish chain 1
draws.shape (200, 1, 12)
inferred shifts (should be r_[:5]) [-0.  1.  2.  3.  4.]
deleting tmpfiles dir: /tmp/tmpm18b7spd
done

which ensures the Stan/Python interop works correctly, so any algorithm in CmdStan exposed via CmdStanPy will work as well.

How slow?

For 5 parameters, the Python-calling model reports ~280us for a gradient evaluation, while a pure Stan model (compare.stan) computing the same thing reports ~4us. This is expected since Stan compiles to plain C++, so the overhead is significant but constant: if we bump up the number of parameters from 5 to 5000, they take ~570 us and 420 us respectively.

TODO

  • use autograd.value_and_grad w/ single C call
  • clean up code w/ better names
  • add layer over cmdstanpy to ease use
  • CI build exes for matrix of cmdstan, python & platforms
  • build wheels packing exes
  • extend for Julia https://docs.julialang.org/en/v1/manual/embedding/?