numpyro-inferutils 0.1.2

Utility functions for extracting log-probabilities, parameter transforms, and Fisher information from NumPyro models.

numpyro-inferutils

Small utility functions for inference with NumPyro models.

This package provides lightweight helpers for:

• extracting log-prior and log-likelihood from NumPyro models,
• working with constrained / unconstrained parameter spaces,
• computing Fisher information matrices from NumPyro models with independent Gaussian likelihoods.
• performing MAP estimation using stochastic variational inference (SVI).

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Installation

pip install numpyro-inferutils

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Quick examples

A minimal NumPyro model

All examples below assume a simple NumPyro model such as:

import numpyro
import numpyro.distributions as dist
import numpy as np

x = np.linspace(-5, 5, 100)
sigma = np.ones_like(x) * np.exp(0.01)
y = 0.5 * x + 1.0 + np.random.randn(len(x)) * sigma

def model(x, y):
    w = numpyro.sample("w", dist.Normal(0.0, 1.0))
    b = numpyro.sample("b", dist.Normal(0.0, 1.0))
    sigma = numpyro.sample("sigma", dist.LogNormal(0.0, 0.01))

    mu = w * x + b
    numpyro.deterministic("mu", mu)

    numpyro.sample("obs", dist.Normal(mu, sigma), obs=y)

Log-prior and log-likelihood

from numpyro_inferutils import build_logprob_functions

logprior, loglik = build_logprob_functions(model, model_kwargs={"x": x, "y": y})

theta = {
    "w": 0.0,
    "b": 1.2,
}

lp = logprior(theta)
ll = loglik(theta)

• logprior(theta) sums log-probabilities from non-observed sample sites.
• loglik(theta) sums log-probabilities from observed sample sites.
• Contributions added via numpyro.factor are treated as part of the log-likelihood.

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Constrained ↔ unconstrained parameters

from numpyro_inferutils.transforms import to_unconstrained_dict

params_constrained = {"sigma": 2.0}
params_unconstrained = to_unconstrained_dict(
    model,
    params_constrained,
    keys=["sigma"],
    x=x, y=y
)

This inspects the model’s sample-site supports and applies the appropriate inverse transforms using

biject_to(site["fn"].support)

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MAP estimation via SVI

For many applications, it is useful to obtain a fast maximum a posteriori (MAP) estimate, for example as an initial point for NUTS.

import jax
from numpyro_inferutils import find_map_svi

rng_key = jax.random.PRNGKey(0)

p_map = find_map_svi(
    model,
    step_size=1e-2,
    num_steps=5_000,
    rng_key=rng_key,
    x=x,
    y=y,
)

• The MAP estimate is obtained via stochastic variational inference (SVI) using a Laplace autoguide (AutoLaplaceApproximation).
• Only a MAP-like point estimate (the guide median) is returned; the covariance of the Laplace approximation is intentionally not used.
• Parameter constraints defined in the NumPyro model are handled automatically.
• The returned parameters are in the constrained space.

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Fisher information (independent Gaussian likelihood)

from numpyro_inferutils.fisher import information_from_model_independent_normal

info = information_from_model_independent_normal(
    model=model,
    pdic={"w": 1.0, "b": 0.5},
    mu_name="mu",
    observed=y,
    model_args=(x, y),
    keys=["w", "b"],
    sigma_sd=sigma,
)

F = info["fisher"]

The Fisher matrix for an independent Gaussian likelihood is computed as

F = Jᵀ J,

where J_ij = ∂r_i / ∂θ_j and

r = (y − μ(θ)) / σ.

Both constrained and unconstrained parameterizations are supported.

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License

MIT License.