morphZ 0.3.7.1

Morph-Z for high accuracy marginal likelihood estimation package.

MorphZ

MorphZ for high accuracy marginal likelihood estimation and morphological density approximation toolkit for scientific workflows, with utilities for dependency analysis.

• Flexible Morph backends: independent, pairwise, grouped, and "tree-structured".
• Bandwidth selection: Scott, Silverman, Botev ISJ, and cross-validation variants.
• Evidence estimation via bridge sampling with robust diagnostics.
• Mutual information and Total correlation estimation.
• Mutual information and Chow–Liu dependency tree visualisation.

Installation

Python 3.10+ is recommended.

pip install morphz

From source (editable):

pip install -e .

Run The Examples

Interactive notebooks live in examples/:

• examples/eggbox.ipynb
• examples/gaussian shell.ipynb
• examples/peak_sampling_new.ipynb

Quick Starts

Minimal Morph fit and evaluate:

import numpy as np
from morphZ import Morph_Indep, select_bandwidth

rng = np.random.default_rng(0)
X = rng.normal(size=(500, 2))

bw = select_bandwidth(X, method="silverman")
morph_indep = Morph_Indep(X, kde_bw=bw)

pts = rng.normal(size=(5, 2))
print(morph_indep.logpdf(pts))

Compute MI heatmap and a Chow–Liu tree (artifacts saved to out_dir):

import numpy as np
from morphZ import dependency_tree

X = np.random.default_rng(0).normal(size=(1000, 4))
mi, tree, edges = dependency_tree.compute_and_plot_mi_tree(
    X, names=["x0", "x1", "x2", "x3"], out_path="out_dir", morph_type="tree"
)
print("Edges (parent -> child):", edges)

Compute n‑order Total Correlation (TC) and save results:

import numpy as np
from morphZ import Nth_TC

X = np.random.default_rng(0).normal(size=(1000, 5))
Nth_TC.compute_and_save_tc(
    X, names=[f"x{i}" for i in range(X.shape[1])], n_order=3, out_path="out_dir"
)

MorphZ evidence estimation using groups:

import numpy as np
from multiprocessing import Pool
from morphZ import evidence

rng = np.random.default_rng(0)
dim = 2

# Toy "posterior": standard Normal, known up to a constant
def log_target(theta: np.ndarray) -> float:
    return -0.5 * np.dot(theta, theta)

# Pretend these came from an MCMC chain
post_samples = rng.normal(size=(5000, dim))
log_post_vals = -0.5 * np.sum(post_samples**2, axis=1)

results = evidence(
    post_samples=post_samples,
    log_posterior_values=log_post_vals,
    log_posterior_function=log_target,
    n_resamples=2000,
    morph_type="2_group",          # "indep" | "pair" | "tree" | "3_group" | ...
    kde_bw="silverman",             # "scott" | "silverman" | "isj" | "cv_iso" | "cv_diag" | numeric
    param_names=[f"x{i}" for i in range(dim)],
    output_path="examples/morphZ_gaussian_demo",
    n_estimations=2,
    verbose=True,
    # Parallelization follows the emcee/pocoMC pool contract:
    # - None: serial (default)
    # - int: internal multiprocessing pool with that many workers
    # - pool-like with .map: external pool, lifecycle owned by the caller
    pool=None,
)

print("log(z), err per run:\n", np.array(results))

Artifacts will be saved under examples/morphZ_gaussian_demo/ (bandwidths, MI/Tree files as needed, and logz_morph_z_<morph_type>_<bw_method>.txt).

To use an external pool (e.g., multiprocessing.Pool) wrap your call:

with Pool() as p:
    results = evidence(..., pool=p)

If you pass pool="max" the library resolves it to os.cpu_count() workers; if you pass an int it creates an internal pool and cleans it up automatically.

Documentation

Jupyter Book powers the project docs. During each build the helper script copies README.md and the contents of examples/ into docs/_auto/ so that the book always reflects the latest files without committing the generated copies.

python -m pip install 'jupyter-book<2'
./docs/build_docs.sh

HTML output is written to docs/_build/html, and GitHub Actions publishes it to GitHub Pages automatically on pushes to main.

API Highlights

• Morphs: Morph_Indep, Morph_Pairwise, Morph_Group, Morph_Tree.
• Bandwidths: select_bandwidth, compute_and_save_bandwidths.
• Evidence: evidence, bridge_sampling_ln (lower‑level), compute_bridge_rmse.
• Dependency analysis: dependency_tree.compute_and_plot_mi_tree.
• Total correlation: Nth_TC.compute_and_save_tc.

Notes:

• If you pass a numeric kde_bw (e.g., 0.9) the library skips bandwidth JSONs.
• pair/group proposals will compute and cache MI.json/params_*_TC.json on first use.

Dependencies

• Core: numpy, scipy, matplotlib, corner, networkx, emcee, statsmodels, scikit-learn
• Optional: pandas (CSV labels), pygraphviz (nicer tree layout), scikit-sparse (optional exception type)

Development

• Build wheels/sdist: python -m build
• Check metadata: twine check dist/*
• Tests live in tests/

Versioning & Release

Versioning is derived from git tags via setuptools_scm.

• Tag a release: git tag vX.Y.Z && git push --tags
• CI: publishes to TestPyPI on pushes to main/master; to PyPI on v* tags.
• Uses PyPI/TestPyPI Trusted Publishing (OIDC). You can also use API tokens if preferred.

License

BSD-3-Clause. See LICENSE for details.