pyBAMBI

pyBAMBI:Resurrecting BAMBI for the pythonic deep learning era
Author:Dark Machines collaboration
Organiser:Will Handley
Version:0.1.0
GitHub:https://github.com/DarkMachines/pyBAMBI
Documentation:https://pybambi.readthedocs.io/
Website:https://darkmachines.org
Paper:https://arxiv.org/abs/1110.2997
Build Status https://circleci.com/gh/DarkMachines/pyBAMBI.svg?style=svg Test Coverage Status Documentation Status https://zenodo.org/badge/157531981.svg

Notes

Currently, there is a test script in test.py, which shows how to call pyBAMBI. The main piece of work to be done is to implement neural network modelling in pybambi/dumper.py from the view onto the live points every nlive iterations.

Installation instructions

You can clone the repository with

git clone https://github.com/DarkMachines/pyBAMBI

Although if you wish to contribute, you should fork the repository to your own account.

You can run the tests with either of the commands:

python -m pytest tests
python setup.py test

Key idea

Use the dumper functions to train a neural network.

pybambi package

Subpackages

pybambi.neuralnetworks package

Submodules

pybambi.neuralnetworks.base module

Base predictor class.

Author: Will Handley (wh260@cam.ac.uk) Date: November 2018

class pybambi.neuralnetworks.base.Predictor(params, logL, split=0.8)[source]

Bases: object

Base predictor class.

This takes in a training set params -> logL, and aims to construct a mapping between them.

Parameters:
  • params – numpy.array of physical parameters to train on shape (ntrain, ndims)
  • logL – numpy.array of loglikelihoods to learn shape (ntrain,)
uncertainty()[source]

Uncertainty value for the trained model.

valid(loglikelihood)[source]

Check validity of proxy.

Checks to see if the supplied log likelihood value is within the current range of likelihoods, including the uncertainty

Parameters:loglikelihood – Value of the log likelihood that needs checking

pybambi.neuralnetworks.kerasnet module

Keras neural net predictor.

This implements a Keras Sequential model (a deep MLP)

Author: Martin White (martin.white@adelaide.edu.au) Date: December 2018

class pybambi.neuralnetworks.kerasnet.KerasNetInterpolation(params, logL, split=0.8, model=None)[source]

Bases: pybambi.neuralnetworks.base.Predictor

Keras neural net interpolation.

Returns the loglikelihood from a Keras neural net-based interpolator

Trains a basic 3-layer neural network with 200 neurons per layer.

Parameters:
  • params – numpy.array of physical parameters to train on shape (ntrain, ndims)
  • logL – numpy.array of loglikelihoods to learn shape (ntrain,)
uncertainty()[source]

Uncertainty value for the trained keras model.

pybambi.neuralnetworks.nearestneighbour module

Nearest neighbour interpolation predictor.

Author: Will Handley (wh260@cam.ac.uk) Date: November 2018

This implements a nearest neighbour interpolation, and is designed as a placeholder predictor, rather than an actual neural network

class pybambi.neuralnetworks.nearestneighbour.NearestNeighbourInterpolation(params, logL, split=0.8)[source]

Bases: pybambi.neuralnetworks.base.Predictor

Nearest Neighbour interpolation.

Returns the loglikelihood of the training point closest in parameter space

Parameters:
  • params – numpy.array of physical parameters to train on shape (ntrain, ndims)
  • logL – numpy.array of loglikelihoods to learn shape (ntrain,)
uncertainty()[source]

Rough uncertainty for the nearest neighbour model.

Module contents

Collection of neural network interpolators.

Author: Will Handley (wh260@cam.ac.uk) Date: November 2018

Submodules

pybambi.bambi module

Driving routine for pyBAMBI.

Author: Will Handley (wh260@cam.ac.uk) Date: November 2018

pybambi.bambi.run_pyBAMBI(loglikelihood, prior, nDims, **kwargs)[source]

Run pyBAMBI.

Parameters:
  • nested_sampler (str) – Choice of nested sampler. Options: [‘multinest’, ‘polychord’]. Default ‘polychord’.
  • nlive (int) – Number of live points. Default nDims*25
  • root (str) – root of filename. Default ‘chains/<nested_sampler>’
  • num_repeats (int) – number of repeats for polychord. Default nDims*5
  • eff (float) – efficiency for multinest. Default 0.5**nDims
  • learner (object) – information indicating what learning algorithm to use for approximating the likelihood. Can be the string ‘keras’, or a keras.models.Model Default ‘keras’
  • ntrain (int) – Number of training points to use Default nlive
  • proxy_tolerance (float) – Required accuracy of proxy. Default 0.01
  • ns_output (int) – Nested sampling output level.

pybambi.manager module

BAMBI management object.

Author: Pat Scott (p.scott@imperial.ac.uk) Date: Feb 2019

class pybambi.manager.BambiManager(loglikelihood, learner, proxy_tolerance, failure_tolerance, ntrain)[source]

Bases: object

Does all the talking for BAMBI.

Takes a new set of training data from the dumper and trains (or retrains) a neural net, and assesses whether or not it can be used for a given parameter combination.

Parameters:ntrain (int) – Number of training points to use
dumper(live_params, live_loglks, dead_params, dead_loglks)[source]

Respond to signal from nested sampler.

loglikelihood(params)[source]

Bambi Proxy wrapper for original loglikelihood.

make_learner(params, loglikes)[source]

Construct a Predictor.

train_new_learner(params, loglikes)[source]

Train a new Predictor.

pybambi.multinest module

Wrapper for PyMultiNest.

Author: Will Handley (wh260@cam.ac.uk) Date: November 2018

pybambi.multinest.run_multinest(loglikelihood, prior, dumper, nDims, nlive, root, ndump, eff)[source]

Run MultiNest.

See https://arxiv.org/abs/0809.3437 for more detail

Parameters:
  • loglikelihood (callable) –

    probability function taking a single parameter:

    • theta: numpy.array
      physical parameters, shape=(nDims,)

    returning a log-likelihood (float)

  • prior (callable) –

    tranformation function taking a single parameter

    • cube: numpy.array
      hypercube parameters, shape=(nDims,)

    returning physical parameters (numpy.array)

  • dumper (callable) –

    access function called every nlive iterations giving a window onto current live points. Single parameter, no return:

    • live:
      numpy.array live parameters and loglikelihoods, shape=(nlive,nDims+1)
  • nDims (int) – Dimensionality of sampling space
  • nlive (int) – Number of live points
  • root (str) – base name for output files
  • ndump (int) – How many iterations between dumper function calls
  • eff (float) – Efficiency of MultiNest

pybambi.polychord module

Wrapper for PyPolyChord.

Author: Will Handley (wh260@cam.ac.uk) Date: November 2018

pybambi.polychord.run_polychord(loglikelihood, prior, dumper, nDims, nlive, root, ndump, num_repeats)[source]

Run PolyChord.

See https://arxiv.org/abs/1506.00171 for more detail

Parameters:
  • loglikelihood (callable) –

    probability function taking a single parameter:

    • theta: numpy.array
      physical parameters, shape=(nDims,)

    returning a log-likelihood (float)

  • prior (callable) –

    tranformation function taking a single parameter

    • cube: numpy.array
      hypercube parameters, shape=(nDims,)

    returning physical parameters (numpy.array)

  • dumper (callable) –

    access function called every nlive iterations giving a window onto current live points. Single parameter, no return:

    • live:
      numpy.array of live parameters and loglikelihoods, shape=(nlive,nDims+1)
  • nDims (int) – Dimensionality of sampling space
  • nlive (int) – Number of live points
  • root (str) – base name for output files
  • ndump (int) – How many iterations between dumper function calls
  • num_repeats (int) – Length of chain to generate new live points

Module contents

Main pyBAMBI module.

Author: Will Handley (wh260@cam.ac.uk) Date: November 2018

Functions

  • run_pyBAMBI

pybambi

pyBAMBI

pyBAMBI:Resurrecting BAMBI for the pythonic deep learning era
Author:Dark Machines collaboration
Organiser:Will Handley
Version:0.1.0
GitHub:https://github.com/DarkMachines/pyBAMBI
Documentation:https://pybambi.readthedocs.io/
Website:https://darkmachines.org
Paper:https://arxiv.org/abs/1110.2997
Build Status https://circleci.com/gh/DarkMachines/pyBAMBI.svg?style=svg Test Coverage Status Documentation Status https://zenodo.org/badge/157531981.svg

Notes

Currently, there is a test script in test.py, which shows how to call pyBAMBI. The main piece of work to be done is to implement neural network modelling in pybambi/dumper.py from the view onto the live points every nlive iterations.

Installation instructions

You can clone the repository with

git clone https://github.com/DarkMachines/pyBAMBI

Although if you wish to contribute, you should fork the repository to your own account.

You can run the tests with either of the commands:

python -m pytest tests
python setup.py test

Key idea

Use the dumper functions to train a neural network.