Guide

This tutorial will walk you through the different steps of using PyCmdStan.

Install

Pycmdstan is a pure-Python package which can be installed from PyPI

pip install --upgrade pycmdstan

or from sources

pip install -e git+https://gitlab.thevirtualbrain.org/tvb/pycmdstan

Basics

import os
os.environ['CMDSTAN'] = '~/src/cmdstan-2.17.1'
from pycmdstan import Model, Run

model = Model('''
data { vector[20] x; real mu; }
parameters { real sig; }
model { x ~ normal(mu, sig); }
generate quantities {
    vector[20] log_lik;
    for (i in 1:20) log_lik[i] = normal_lpdf(x[i] | mu, sig);
}
''')

runs = model.sample(
        data=dict(mu, **data),
        chains=4
)
assert runs.N_eff_per_iter.min() > 0.2
assert runs.R_hat.max() < 1.2

data = {'x': np.random.randn(20) + 5.0}
loo = []
mus = np.r_[1.0, 3.0, 5.0, 7.0, 9.0]
for mu in mus:
    run = model.sample(
        data=dict(mu=mu, **data), num_warmup=200, num_samples=200)
    loo.append(run['loo'])
assert mus[np.argmin(loo)] == 5.0

CmdStan’s command line arguments are structured as a tree, so while child parameters of the method argument can be passed directly,

model.sample(num_samples=500)

equivalent to

./$model sample num_samples=500

A more complex case with nested parameters looks like

./$model id=$i \
    sample save_warmup=1 num_warmup=200 num_samples=200 \
        adapt \
            delta=0.8 \
        algorithm=hmc \
            engine=nuts \
                max_depth=12

Pycmdstan doesn’t do anything clever (yet), so full set of subarguments need to be passed as equivalent strings

model.sample(
        save_warmup=1,
        num_warmup=200,
        num_samples=200,
        adapt_='delta=0.8',
        algorithm='hmc engine=nuts max_depth=12')

Here, the _ postfix on adapt_ means adapt doesn’t take a value, but subarguments. In doubt, the command line used to call the model is available as an attribute of the Run instance,

run = model.sample(...)
print(run.cmd)

Plots

(todo)

Trace plots

Parallel coordinates plot