bilby.core.sampler.base_sampler.Sampler

class bilby.core.sampler.base_sampler.Sampler(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, skip_import_verification=False, injection_parameters=None, meta_data=None, result_class=None, likelihood_benchmark=False, soft_init=False, exit_code=130, npool=1, **kwargs)[source]

Bases: object

A sampler object to aid in setting up an inference run

Parameters:

likelihood: likelihood.Likelihood: A object with a log_l method
priors: bilby.core.prior.PriorDict, dict: Priors to be used in the search. This has attributes for each parameter to be sampled.
external_sampler: str, Sampler, optional: A string containing the module name of the sampler or an instance of this class
outdir: str, optional: Name of the output directory
label: str, optional: Naming scheme of the output files
use_ratio: bool, optional: Switch to set whether or not you want to use the log-likelihood ratio or just the log-likelihood
plot: bool, optional: Switch to set whether or not you want to create traceplots
injection_parameters:: A dictionary of the injection parameters
meta_data:: A dictionary of extra meta data to store in the result
result_class: bilby.core.result.Result, or child of: The result class to use. By default, bilby.core.result.Result is used, but objects which inherit from this class can be given providing additional methods.
soft_init: bool, optional: Switch to enable a soft initialization that prevents the likelihood from being tested before running the sampler. This is relevant when using custom likelihoods that must NOT be initialized on the main thread when using multiprocessing, e.g. when using tensorflow in the likelihood.
**kwargs: dict: Additional keyword arguments

Attributes:

likelihood: likelihood.Likelihood: A object with a log_l method
priors: bilby.core.prior.PriorDict: Priors to be used in the search. This has attributes for each parameter to be sampled.
external_sampler: Module: An external module containing an implementation of a sampler.
outdir: str: Name of the output directory
label: str: Naming scheme of the output files
use_ratio: bool: Switch to set whether or not you want to use the log-likelihood ratio or just the log-likelihood
plot: bool: Switch to set whether or not you want to create traceplots
skip_import_verification: bool: Skips the check if the sampler is installed if true. This is only advisable for testing environments
result: bilby.core.result.Result: Container for the results of the sampling run
exit_code: int: System exit code to return on interrupt
kwargs: dict: Dictionary of keyword arguments that can be used in the external sampler
hard_exit: bool: Whether the implemented sampler exits hard (os._exit rather than sys.exit). The latter can be escaped as SystemExit. The former cannot.
sampler_namestr: Name of the sampler. This is used when creating the output directory for the sampler.
abbreviationstr: Abbreviated name of the sampler. Does not have to be specified in child classes. If set to a value other than None, this will be used instead of sampler_name when creating the output directory.

Raises:

TypeError:: If external_sampler is neither a string nor an instance of this class If not all likelihood.parameters have been defined
ImportError:: If the external_sampler string does not refer to a sampler that is installed on this system
AttributeError:: If some of the priors can’t be sampled

__init__(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, skip_import_verification=False, injection_parameters=None, meta_data=None, result_class=None, likelihood_benchmark=False, soft_init=False, exit_code=130, npool=1, **kwargs)[source]

__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_expected_outputs`([outdir, label])	Get lists of the expected outputs directories and files.
`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)
`log_prior`(theta)
`prior_transform`(theta)	Prior transform method that is passed into the external sampler.
`run_sampler`()	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

`abbreviation`
`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
`npool`
`npool_equiv_kwargs`
`sampler_name`
`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

check_draw(theta, warning=True)[source]

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

classmethod get_expected_outputs(outdir=None, label=None)[source]

Get lists of the expected outputs directories and files.

These are used by bilby_pipe when transferring files via HTCondor. Both can be empty. Defaults to a single directory: "{outdir}/{name}_{label}/", where name is abbreviation if it is defined for the sampler class, otherwise it defaults to sampler_name.

Parameters:

outdirstr: The output directory.
labelstr: The label for the run.

Returns:

list: List of file names.
list: List of directory names.

get_initial_points_from_prior(npoints=1)[source]

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()[source]

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)[source]

Parameters:

theta: list: List of values for the likelihood parameters

Returns:

float: Log-likelihood or log-likelihood-ratio given the current: likelihood.parameter values

log_prior(theta)[source]

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)[source]

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

run_sampler()[source]: A template method to run in subclasses

sampling_seed_key = None: 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)[source]

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.