margarine 2.0.1

margarine: Posterior Sampling and Marginal Bayesian Statistics

margarine: density estimation made easy

Authors: Harry T.J. Bevins
Version: 2.0.1
Homepage: https://github.com/htjb/margarine
Documentation: https://margarine.readthedocs.io/

margarine provides a suite of density estimation tools including KDEs, normalizing flows like NICE and RealNVP as well as a novel method for improved performance on multimodal distributions.

The code can be used to:

• Emulate posterior distributions from weightened samples (e.g. MCMC, nested sampling)
• Build non-trivial priors from samples
• Perform density estimation tasks in general machine learning applications
• Emulate correctly normalised marginal likelihoods
• Calculate statistics like the KL divergence between different density estimators and marginal model dimensionalities.

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Installation

From version 2.0.0 margarine moved to JAX for improved performance. Older versions (1.x.x) using TensorFlow are still available via pip with the last release being 1.4.2.

Install from Git:

git clone https://github.com/htjb/margarine.git  # or use SSH
cd margarine
pip install .

Or via pip:

pip install margarine

Note: pip may not always give the latest version.

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Getting Started

All of the density estimators in margarine have a common interface and set of methods including train(), sample(), log_prob(), log_like(), save() and load(). The below example shows how to train a RealNVP and generate samples.

import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt

from margarine.estimators.realnvp import RealNVP

nsamples = 5000
key = jax.random.PRNGKey(0)

original_samples = jax.random.multivariate_normal(
    key,
    mean=jnp.array([0.0, 0.0]),
    cov=jnp.array([[1.0, 0.8], [0.8, 1.0]]),
    shape=(nsamples,),
)

weights = jnp.ones(len(original_samples))

realnvp_estimator = RealNVP(
        original_samples,
        weights=weights,
        in_size=2,
        hidden_size=50,
        num_layers=6,
        num_coupling_layers=6,
    )

key, subkey = jax.random.split(key)

realnvp_estimator.train(
            subkey,
            learning_rate=1e-3,
            epochs=2000,
            patience=50,
            batch_size=1000,
        )

generated_samples = realnvp_estimator.sample(key, num_samples=nsamples)

plt.scatter(
    original_samples[:, 0], original_samples[:, 1], alpha=0.5, label="Original Samples"
)
plt.scatter(
    generated_samples[:, 0], generated_samples[:, 1], alpha=0.5, label="Generated Samples"
)
plt.legend()
plt.title("RealNVP: Original vs Generated Samples")
plt.xlabel("X1")
plt.ylabel("X2")
plt.show()

for more details see the documentation.

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Documentation

Available at: https://margarine.readthedocs.io/. To build locally:

pip install ".[docs]"
mkdocs serve

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Licence & Citation

Licensed under MIT.

If used for academic work, please cite:

• Main paper: https://arxiv.org/abs/2205.12841
• MaxEnt22 proceedings: https://arxiv.org/abs/2207.11457
• Piecewise Normalising Flows Paper: https://arxiv.org/abs/2305.02930

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Contributing

Contributions and feature suggestions welcome. Open an issue to report bugs or discuss ideas. See CONTRIBUTING.md for details.

The future goals of the project are outlined in ROADMAP.md.