bilby.core.sampler.dnest4.DNest4

class bilby.core.sampler.dnest4.DNest4(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, exit_code=77, skip_import_verification=False, temporary_directory=True, **kwargs)

Bases: _TemporaryFileSamplerMixin, NestedSampler

Bilby wrapper of DNest4

Parameters:

TBD

Other Parameters

——==========

num_particles: int

The number of points to use in the Nested Sampling active population.

max_num_levels: int

The max number of diffusive likelihood levels that DNest4 should initialize during the Diffusive Nested Sampling run.

backend: str

The python DNest4 backend for storing the output. Options are: ‘memory’ and ‘csv’. If ‘memory’ the DNest4 outputs are stored in memory during the run. If ‘csv’ the DNest4 outputs are written out to files with a CSV format during the run. CSV backend may not be functional right now (October 2020)

num_steps: int

The number of MCMC iterations to run

new_level_interval: int

The number of moves to run before creating a new diffusive likelihood level

lam: float

Set the backtracking scale length

beta: float

Set the strength of effect to force the histogram to equal bin counts

seed: int

Set the seed for the C++ random number generator

verbose: Bool

If True, prints information during run

__init__(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, exit_code=77, skip_import_verification=False, temporary_directory=True, **kwargs)

__call__(*args, **kwargs)

Call self as a function.

Methods

__init__(likelihood, priors[, outdir, ...])
calc_likelihood_count()
check_draw(theta[, warning])	Checks if the draw will generate an infinite prior or likelihood
get_initial_points_from_prior([npoints])	Method to draw a set of live points from the prior
get_random_draw_from_prior()	Get a random draw from the prior distribution
log_likelihood(theta)	Since some nested samplers don't call the log_prior method, evaluate the prior constraint here.
log_prior(theta)	Parameters:
prior_transform(theta)	Prior transform method that is passed into the external sampler.
reorder_loglikelihoods(...)	Reorders the stored log-likelihood after they have been reweighted
run_sampler(*args, **kwargs)	A template method to run in subclasses
write_current_state()
write_current_state_and_exit([signum, frame])	Make sure that if a pool of jobs is running only the parent tries to checkpoint and exit.

Attributes

check_point_equiv_kwargs
constraint_parameter_keys	list: List of parameters providing prior constraints
default_kwargs
external_sampler_name
fixed_parameter_keys	list: List of parameter keys that are not being sampled
hard_exit
kwargs	dict: Container for the kwargs.
ndim	int: Number of dimensions of the search parameter space
npoints_equiv_kwargs
npool
npool_equiv_kwargs
outputfiles_basename
sampling_seed_equiv_kwargs
sampling_seed_key	Name of keyword argument for setting the sampling for the specific sampler.
search_parameter_keys	list: List of parameter keys that are being sampled
short_name
temporary_outputfiles_basename
walks_equiv_kwargs

check_draw(theta, warning=True)

Checks if the draw will generate an infinite prior or likelihood

Also catches the output of numpy.nan_to_num.

Parameters:

theta: array_like

Parameter values at which to evaluate likelihood

warning: bool

Whether or not to print a warning

Returns:

bool, cube (nlive,

True if the likelihood and prior are finite, false otherwise

property constraint_parameter_keys

list: List of parameters providing prior constraints

property fixed_parameter_keys

list: List of parameter keys that are not being sampled

get_initial_points_from_prior(npoints=1)

Method to draw a set of live points from the prior

This iterates over draws from the prior until all the samples have a finite prior and likelihood (relevant for constrained priors).

Parameters:

npoints: int

The number of values to return

Returns:

unit_cube, parameters, likelihood: tuple of array_like

unit_cube (nlive, ndim) is an array of the prior samples from the unit cube, parameters (nlive, ndim) is the unit_cube array transformed to the target space, while likelihood (nlive) are the likelihood evaluations.

get_random_draw_from_prior()

Get a random draw from the prior distribution

Returns:

draw: array_like

An ndim-length array of values drawn from the prior. Parameters with delta-function (or fixed) priors are not returned

property kwargs

dict: Container for the kwargs. Has more sophisticated logic in subclasses

log_likelihood(theta)

Since some nested samplers don’t call the log_prior method, evaluate the prior constraint here.

Parameters:

theta: array_like

Parameter values at which to evaluate likelihood

Returns:

float: log_likelihood

log_prior(theta)

Parameters:

theta: list

List of sampled values on a unit interval

Returns:

float: Joint ln prior probability of theta

property ndim

int: Number of dimensions of the search parameter space

prior_transform(theta)

Prior transform method that is passed into the external sampler.

Parameters:

theta: list

List of sampled values on a unit interval

Returns:

list: Properly rescaled sampled values

static reorder_loglikelihoods(unsorted_loglikelihoods, unsorted_samples, sorted_samples)

Reorders the stored log-likelihood after they have been reweighted

This creates a sorting index by matching the reweights result.samples against the raw samples, then uses this index to sort the loglikelihoods

Parameters:

sorted_samples, unsorted_samples: array-like

Sorted and unsorted values of the samples. These should be of the same shape and contain the same sample values, but in different orders

unsorted_loglikelihoods: array-like

The loglikelihoods corresponding to the unsorted_samples

Returns:

sorted_loglikelihoods: array-like

The loglikelihoods reordered to match that of the sorted_samples

run_sampler(*args, **kwargs)

A template method to run in subclasses

sampling_seed_key = 'seed'

Name of keyword argument for setting the sampling for the specific sampler. If a specific sampler does not have a sampling seed option, then it should be left as None.

property search_parameter_keys

list: List of parameter keys that are being sampled

write_current_state_and_exit(signum=None, frame=None)

Make sure that if a pool of jobs is running only the parent tries to checkpoint and exit. Only the parent has a ‘pool’ attribute.

For samplers that must hard exit (typically due to non-Python process) use os._exit that cannot be excepted. Other samplers exiting can be caught as a SystemExit.