pybambi package

Subpackages

• pybambi.neuralnetworks package
  • Submodules
  • pybambi.neuralnetworks.base module
  • pybambi.neuralnetworks.kerasnet module
  • pybambi.neuralnetworks.nearestneighbour module
  • Module contents

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)

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)

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)

Respond to signal from nested sampler.

loglikelihood(params)

Bambi Proxy wrapper for original loglikelihood.

make_learner(params, loglikes)

Construct a Predictor.

train_new_learner(params, loglikes)

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, seed=-1)

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
• seed (int) – Seed for sampler. Optional, no default seed.

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, seed=-1)

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
• seed (int) – Seed for sampler. Optional, no default seed.

Module contents

Main pyBAMBI module.

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

Functions

• run_pyBAMBI