Exports ARViz InferenceData to JSON + binary npy arrays for client-side use. Intended to make it possible to access the outputs of Bayesian inference in interactive JS applications in the browser. This packages up the data in the NetCDF/xarray block into a zip file containing JSON metadata, and a collection of npy format arrays. A loader to unpack this format in Javascript is provided.
Multiple models can be packed into a single zip file using multi_arviz_to_json() which can be conveniently loaded in the browser.
The module also includes functionality to extract a very basic skeleton of the DAG from PyMC3, giving the parents of each variable and basic information about dimension and distribution type, and can package this alongside the model.
from arviz_json import get_dag, arviz_to_json
...
with model:
# Get posterior trace, prior trace, posterior predictive samples, and the DAG
trace = pm.sample(samples=samples, chains=chains)
prior = pm.sample_prior_predictive(samples=samples)
posterior_predictive = pm.sample_posterior_predictive(trace, predictive_samples, model)
dag = get_dag(model)
data = az.from_pymc3(trace=trace, prior=prior, posterior_predictive=posterior_predictive)
# insert variable graph into sampler stat attributes
data.sample_stats.attrs["graph"] = dag
arviz_to_json(data, "switchpoint.npz")And this data can then can be loaded in-browser:
load_npz("switchpoint.npz").then(function(npz_data)
{
arviz_data = reassemble_arviz(npz_data);
console.log(arviz_data.observed); // observations
console.log(arviz_data.posterior); // posterior samples
console.log(arviz_data.prior); // prior samples
console.log(arviz_data.sample_stats); // statistics
console.log(arviz_data.sample_stats.dag); // graph of model
console.log(arviz_data.posterior_predictive); // p. predictive samples
});