bilby.core.sampler.pymc.Pymc

class bilby.core.sampler.pymc.Pymc(likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False, skip_import_verification=False, **kwargs)

Bases: MCMCSampler

bilby wrapper of the PyMC sampler (https://www.pymc.io/)

All keyword arguments (i.e., the kwargs) passed to run_sampler will be propapated to pymc.sample where appropriate, see documentation for that class for further help. Under Other Parameters, we list commonly used kwargs and the bilby, or where appropriate, PyMC defaults.

Parameters:

draws: int, (1000)

The number of sample draws from the posterior per chain.

chains: int, (2)

The number of independent MCMC chains to run.

cores: int, (1)

The number of CPU cores to use.

tune: int, (500)

The number of tuning (or burn-in) samples per chain.

discard_tuned_samples: bool, True

Set whether to automatically discard the tuning samples from the final chains.

step: str, dict

Provide a step method name, or dictionary of step method names keyed to particular variable names (these are case insensitive). If passing a dictionary of methods, the values keyed on particular variables can be lists of methods to form compound steps. If no method is provided for any particular variable then PyMC will automatically decide upon a default, with the first option being the NUTS sampler. The currently allowed methods are ‘NUTS’, ‘HamiltonianMC’, ‘Metropolis’, ‘BinaryMetropolis’, ‘BinaryGibbsMetropolis’, ‘Slice’, and ‘CategoricalGibbsMetropolis’. Note: you cannot provide a PyMC step method function itself here as it is outside of the model context manager.

step_kwargs: dict

Options for steps methods other than NUTS. The dictionary is keyed on lowercase step method names with values being dictionaries of keywords for the given step method.

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

__call__(*args, **kwargs)

Call self as a function.

Methods

__init__(likelihood, priors[, outdir, ...])
calc_likelihood_count()
calculate_autocorrelation(samples[, c])	Uses the emcee.autocorr module to estimate the autocorrelation
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)	Parameters:
log_prior(theta)	Parameters:
print_nburn_logging_info()	Prints logging info as to how nburn was calculated
prior_transform(theta)	Prior transform method that is passed into the external sampler.
run_sampler()	A template method to run in subclasses
set_likelihood()	Convert any bilby likelihoods to PyMC distributions.
set_prior()	Set the PyMC prior distributions.
setup_prior_mapping()	Set the mapping between predefined bilby priors and the equivalent PyMC distributions.
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
default_nuts_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.
nburn_equiv_kwargs
ndim	int: Number of dimensions of the search parameter space
npool
npool_equiv_kwargs
nwalkers_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

calculate_autocorrelation(samples, c=3)

Uses the emcee.autocorr module to estimate the autocorrelation

Parameters:

samples: array_like

A chain of samples.

c: float

The minimum number of autocorrelation times needed to trust the estimate (default: 3). See emcee.autocorr.integrated_time.

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

classmethod get_expected_outputs(outdir=None, label=None)

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)

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)

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)

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

print_nburn_logging_info()

Prints logging info as to how nburn was calculated

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

run_sampler()

A template method to run in subclasses

sampling_seed_key = 'random_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

set_likelihood()

Convert any bilby likelihoods to PyMC distributions.

set_prior()

Set the PyMC prior distributions.

setup_prior_mapping()

Set the mapping between predefined bilby priors and the equivalent PyMC distributions.

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.