aspire-inference 0.1.0a20

Accelerate Sequential Posterior Inference via REuse

aspire: Accelerated Sequential Posterior Inference via REuse

aspire is a framework for reusing existing posterior samples to obtain new results at a reduced cost.

Installation

aspire can be installed from PyPI using pip. By default, you need to install one of the backends for the normalizing flows, either torch or jax. We also recommend installing minipcn if using the smc sampler:

Torch

We recommend installing torch manually to ensure correct CPU/CUDA versions are installed. See the PyTorch installation instructions for more details.

pip install aspire-inference[torch,minipcn]

Jax:

We recommend install jax manually to ensure the correct GPU/CUDA versions are installed. See the jax documentation for details

pip install aspire-inference[jax,minipcn]

Important: the name of aspire on PyPI is aspire-inference but once installed the package can be imported and used as aspire.

Quickstart

import numpy as np
from aspire import Aspire, Samples

# Define a log-likelihood and log-prior
def log_likelihood(samples):
    x = samples.x
    return -0.5 * np.sum(x**2, axis=-1)

def log_prior(samples):
    return -0.5 * np.sum(samples.x**2, axis=-1)

# Create the initial samples
init = Samples(np.random.normal(size=(2_000, 4)))

# Define the aspire object
aspire = Aspire(
    log_likelihood=log_likelihood,
    log_prior=log_prior,
    dims=4,
    parameters=[f"x{i}" for i in range(4)],
)

# Fit the normalizing flow
aspire.fit(init, n_epochs=20)

# Sample the posterior
posterior = aspire.sample_posterior(
    sampler="smc",
    n_samples=500,
    sampler_kwargs=dict(n_steps=100),
)

# Plot the posterior distribution
posterior.plot_corner()

Documentation

See the documentation on ReadTheDocs.

Citation

If you use aspire in your work please cite the DOI and paper.