firetruck 0.1.0

NumPyro dialect for mental midgets

🚒 firetruck

!!! NOTE: This repo is just an experiment for now, not ready for any kind of serious use. The package is not published on PyPI either !!!

firetruck is NumPyro dialect for mental midgets. This means:

• No numpyro.deterministic and numpyro.sample, just write your code like a normal human, and assign variables you want to track to self
• You can just return your outcome variable from the function, no obs bullshit!
• Greatly simplified sampling and VI. No bespoke solutions, just good defaults for 90% of your use cases.
• You can deal with latent categorical variables without having to do anything, yaaay!
• WebGL accelarated Plotly plots. You don't know ArViz, Matplotlib or any of that jazz. It not only looks better but it's also interactive and faster.

Example

I modified the Waffle House example in the NumPyro docs to use firetruck.

import jax as jax
import jax.numpy as jnp
import numpyro.distributions as dist
import pandas as pd

import firetruck as ftr

DATASET_URL = "https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/WaffleDivorce.csv"
dset = pd.read_csv(DATASET_URL, sep=";")
marriage = jnp.array(dset["Marriage"])
divorce = jnp.array(dset["Divorce"])
age = jnp.array(dset["MedianAgeMarriage"])


# Don't forget this decorator!! Very important
@ftr.compact
def model(self, marriage, age):
    # Just assign variables to self that you want to track,
    # And they will be named automatically!!
    self.a = dist.Normal(0.0, 0.2)
    self.bM = dist.Normal(0.0, 0.5)
    self.bA = dist.Normal(0.0, 0.5)
    self.sigma = dist.Exponential(0.5)
    mu = self.a + self.bM * marriage + self.bA * age
    return dist.Normal(mu, self.sigma)


# Sampling Prior predictive distribution
rng_key = jax.random.key(42)
rng_key, subkey = jax.random.split(rng_key)
prior_predictive = model.add_input(marriage, age).sample_predictive(subkey)

# Add inputs to the model and condition on the output
conditioned_model = model.add_input(marriage, age).condition_on(divorce)

# Fit model using meanfield VI
rng_key, subkey = jax.random.split(rng_key)
res = conditioned_model.meanfield_vi(subkey)

# Sample from model using NUTS
rng_key, subkey = jax.random.split(rng_key)
mcmc = conditioned_model.sample_posterior(subkey)

# Prints this automatically, cause why the hell would you not need this:
#                 mean       std    median      5.0%     95.0%     n_eff     r_hat
#          a      0.01      0.20      0.02     -0.31      0.35   2348.61      1.00
#         bA      0.17      0.05      0.17      0.10      0.25   1634.25      1.00
#         bM      0.26      0.06      0.26      0.16      0.35   1649.36      1.00
#      sigma      1.82      0.19      1.81      1.52      2.11   2419.83      1.00
#
# Number of divergences: 0

# Plot sampling trace
fig = ftr.plot_trace(mcmc)
fig.show()

# Forest plot of posterior samples
fig = ftr.plot_forest(mcmc)
fig.show()

# Forest plot of posterior samples
fig = ftr.plot_ess(mcmc)
fig.show()

# Sampling prior predictive and plotting prior-predictive check
rng_key, subkey = jax.random.split(rng_key)
# NOTE that I'm using the unconditoned model
prior_predictive = model.add_input(marriage, age).sample_predictive(rng_key)
fig = ftr.plot_predictive_check(prior_predictive, obs=divorce)
fig.show()

# Sampling posterior predictive and plotting prior-predictive check
rng_key, subkey = jax.random.split(rng_key)
# Note that I'm passing the posterior_samples to the function
posterior_predictive = model.add_input(marriage, age).sample_predictive(
    rng_key, posterior_samples=mcmc.get_samples()
)
fig = ftr.plot_predictive_check(posterior_predictive, obs=divorce)
fig.show()