pytransc 0.3.0a1

Python library for implementing TransC MCMC sampling

pytransc

Python library for implementing TransC MCMC sampling

This repository contains source code to implement three Trans-Conceptual MCMC sampling algorithms as described in the article Sambridge, Valentine and Hauser (2025).

Installation

pip install git+https://github.com/inlab-geo/pytransc

Documentation

This package of with a single class TransC_Sampler implementing three separate MCMC samplers across independent model states implemented as functions of the class

run_product_space_sampler() - implements a fixed dimension MCMC sampler over the product space of the states, and extracts a TransC/TransD ensemble.

run_state_jump_sampler() - implements an RJ-MCMC style algorithm using pseudo-prior proposals and balance conditions.

run_ensemble_resampler() - implements a single parameter Metropolis sampler over the state indicator variable. Requires posterior ensembles in each state to be precomputed.

Other utility functions include:

run_mcmc_per_state() - performs MCMC sampling within each state.

build_auto_pseudo_prior() - fits a mixture model to posterior ensembles in each state to act as a pseudo-prior function.

get_transc_samples() - creates posterior TransC/TransD ensemble from results of any sample.

Here is the docstring of the function run_state_jump_sampler():

"""Run MCMC sampler over independent states using pre-computed ensembles.

This function performs trans-conceptual MCMC by resampling from pre-computed
posterior ensembles in each state. It calculates relative evidence of each state
by sampling over the ensemble members according to their posterior and pseudo-prior
densities.

Parameters
----------
n_walkers : int
    Number of random walkers used by the ensemble resampler.
n_steps : int
    Number of Markov chain steps to perform per walker.
n_states : int
    Number of independent states in the problem.
n_dims : list of int
    List of parameter dimensions for each state.
log_posterior_ens : StateOrderedEnsemble
    Log-posterior values of ensemble members in each state.
    Format: list of arrays, where each array contains log-posterior values
    for the ensemble members in that state.
log_pseudo_prior_ens : StateOrderedEnsemble
    Log-pseudo-prior values of ensemble members in each state.
    Format: list of arrays, where each array contains log-pseudo-prior values
    for the ensemble members in that state.
seed : int, optional
    Random number seed for reproducible results. Default is 61254557.
state_proposal_weights : list of list of float, optional
    Weights for proposing transitions between states. Should be a matrix
    where element [i][j] is the weight for proposing state j from state i.
    Diagonal elements are ignored. If None, uniform weights are used.
progress : bool, optional
    Whether to display progress information. Default is False.
walker_pool : Any | None, optional
    User-provided pool for parallelizing walker execution. The pool must
    implement a map() method compatible with the standard library's map()
    function. Default is None.
state_pool : Any | None, optional
    User-provided pool for parallelizing state-level operations such as
    pseudo-prior evaluation across states. Currently reserved for future
    enhancements. Default is None.
forward_pool : Any | None, optional
    User-provided pool for parallelizing forward solver calls within
    log_posterior evaluations. If provided, the pool will be made available
    to log_posterior functions via get_forward_pool() from pytransc.utils.forward_context.
    The pool must implement a map() method compatible with the standard library's 
    map() function. Supports ProcessPoolExecutor, ThreadPoolExecutor, 
    and schwimmbad pools. Default is None.

Returns
-------
MultiWalkerEnsembleResamplerChain
    Chain results containing state sequences, ensemble member indices,
    and diagnostics for all walkers.

Notes
-----
This method requires pre-computed posterior ensembles and their corresponding
log-density values. The ensembles can be generated using `run_mcmc_per_state()`
and the pseudo-prior values using automatic fitting routines.

The algorithm works by:
1. Selecting ensemble members within states based on posterior weights
2. Proposing transitions between states based on relative evidence
3. Accepting/rejecting proposals using Metropolis-Hastings criterion

Examples
--------
>>> results = run_ensemble_resampler(
...     n_walkers=32,
...     n_steps=1000,
...     n_states=3,
...     n_dims=[2, 3, 1],
...     log_posterior_ens=posterior_ensembles,
...     log_pseudo_prior_ens=pseudo_prior_ensembles
... )

Using with forward pool for parallel forward solver calls:

>>> from concurrent.futures import ProcessPoolExecutor
>>> with ProcessPoolExecutor(max_workers=4) as forward_pool:
...     results = run_ensemble_resampler(
...         n_walkers=32,
...         n_steps=1000,
...         n_states=3,
...         n_dims=[2, 3, 1],
...         log_posterior_ens=posterior_ensembles,
...         log_pseudo_prior_ens=pseudo_prior_ensembles,
...         forward_pool=forward_pool
...     )
"""

Example

import numpy as np
from pytransc.samplers import run_state_jump_sampler

Detailed examples of showing implementation of all three samplers can be found in

examples/Regression - Sampling across 4 states in polynomial regression with all three samplers.

examples/AirborneEM - Ensemble Sampler applied to Airborne EM data.

examples/Tomography - Ensemble Sampler applied to 2D borehole tomography to demonstrate three level parallelism.

Licensing

pytransc is released as BSD-2-Clause licence.

Citations and Acknowledgments

Sambridge, M., Valentine, A. & Hauser, J., 2025. Trans-Conceptual Sampling: Bayesian Inference With Competing Assumptions, JGR Solid Earth, Volume 130, Issue 8, 17 August 2025, e2024JB030470.