bilby.core.sampler.ptemcee.Ptemcee

class bilby.core.sampler.ptemcee.Ptemcee(likelihood, priors, outdir='outdir', label='label', use_ratio=False, check_point_plot=True, skip_import_verification=False, resume=True, nsamples=5000, burn_in_nact=50, burn_in_fixed_discard=0, mean_logl_frac=0.01, thin_by_nact=0.5, autocorr_tol=50, autocorr_c=5, safety=1, autocorr_tau=1, gradient_tau=0.1, gradient_mean_log_posterior=0.1, Q_tol=1.02, min_tau=1, check_point_delta_t=600, threads=1, exit_code=77, plot=False, store_walkers=False, ignore_keys_for_tau=None, pos0='prior', niterations_per_check=5, log10beta_min=None, verbose=True, **kwargs)

Bases: MCMCSampler

bilby wrapper ptemcee (https://github.com/willvousden/ptemcee)

All positional and keyword arguments (i.e., the args and kwargs) passed to run_sampler will be propagated to ptemcee.Sampler, see documentation for that class for further help. Under Other Parameters, we list commonly used kwargs and the bilby defaults.

Parameters:

nsamples: int, (5000)

The requested number of samples. Note, in cases where the autocorrelation parameter is difficult to measure, it is possible to end up with more than nsamples.

burn_in_nact, thin_by_nact: int, (50, 1)

The number of burn-in autocorrelation times to discard and the thin-by factor. Increasing burn_in_nact increases the time required for burn-in. Increasing thin_by_nact increases the time required to obtain nsamples.

burn_in_fixed_discard: int (0)

A fixed number of samples to discard for burn-in

mean_logl_frac: float, (0.0.1)

The maximum fractional change the mean log-likelihood to accept

autocorr_tol: int, (50)

The minimum number of autocorrelation times needed to trust the estimate of the autocorrelation time.

autocorr_c: int, (5)

The step size for the window search used by emcee.autocorr.integrated_time

safety: int, (1)

A multiplicative factor for the estimated autocorrelation. Useful for cases where non-convergence can be observed by eye but the automated tools are failing.

autocorr_tau: int, (1)

The number of autocorrelation times to use in assessing if the autocorrelation time is stable.

gradient_tau: float, (0.1)

The maximum (smoothed) local gradient of the ACT estimate to allow. This ensures the ACT estimate is stable before finishing sampling.

gradient_mean_log_posterior: float, (0.1)

The maximum (smoothed) local gradient of the logliklilhood to allow. This ensures the ACT estimate is stable before finishing sampling.

Q_tol: float (1.01)

The maximum between-chain to within-chain tolerance allowed (akin to the Gelman-Rubin statistic).

min_tau: int, (1)

A minimum tau (autocorrelation time) to accept.

check_point_delta_t: float, (600)

The period with which to checkpoint (in seconds).

threads: int, (1)

If threads > 1, a MultiPool object is setup and used.

exit_code: int, (77)

The code on which the sampler exits.

store_walkers: bool (False)

If true, store the unthinned, unburnt chains in the result. Note, this is not recommended for cases where tau is large.

ignore_keys_for_tau: str

A pattern used to ignore keys in estimating the autocorrelation time.

pos0: str, list, np.ndarray, dict

If a string, one of “prior” or “minimize”. For “prior”, the initial positions of the sampler are drawn from the sampler. If “minimize”, a scipy.optimize step is applied to all parameters a number of times. The walkers are then initialized from the range of values obtained. If a list, for the keys in the list the optimization step is applied, otherwise the initial points are drawn from the prior. If a numpy array the shape should be (ntemps, nwalkers, ndim). If a dict, this should be a dictionary with keys matching the search_parameter_keys. Each entry should be an array with shape (ntemps, nwalkers).

niterations_per_check: int (5)

The number of iteration steps to take before checking ACT. This effectively pre-thins the chains. Larger values reduce the per-eval timing due to improved efficiency. But, if it is made too large the pre-thinning may be overly aggressive effectively wasting compute-time. If you see tau=1, then niterations_per_check is likely too large.

Other Parameters:

nwalkers: int, (200)

The number of walkers

nsteps: int, (100)

The number of steps to take

ntemps: int (10)

The number of temperatures used by ptemcee

Tmax: float

The maximum temperature

__init__(likelihood, priors, outdir='outdir', label='label', use_ratio=False, check_point_plot=True, skip_import_verification=False, resume=True, nsamples=5000, burn_in_nact=50, burn_in_fixed_discard=0, mean_logl_frac=0.01, thin_by_nact=0.5, autocorr_tol=50, autocorr_c=5, safety=1, autocorr_tau=1, gradient_tau=0.1, gradient_mean_log_posterior=0.1, Q_tol=1.02, min_tau=1, check_point_delta_t=600, threads=1, exit_code=77, plot=False, store_walkers=False, ignore_keys_for_tau=None, pos0='prior', niterations_per_check=5, log10beta_min=None, verbose=True, **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_initial_points_from_prior([npoints])	Method to draw a set of live points from the prior
get_pos0()	Master logic for setting pos0
get_pos0_from_array()
get_pos0_from_dict()	Initialize the starting points from a passed dictionary.
get_pos0_from_minimize([minimize_list])	Draw the initial positions using an initial minimization step
get_pos0_from_prior()	Draw the initial positions from the prior
get_random_draw_from_prior()	Get a random draw from the prior distribution
get_zero_array()
get_zero_chain_array()
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(*args, **kwargs)	A template method to run in subclasses
setup_sampler()	Either initialize the sampler or read in the resume file
write_current_state([plot])
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.
nburn_equiv_kwargs
ndim	int: Number of dimensions of the search parameter space
npool
npool_equiv_kwargs
nwalkers_equiv_kwargs
sampler_function_kwargs	Kwargs passed to samper.sampler()
sampler_init_kwargs	Kwargs passed to initialize ptemcee.Sampler()
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

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_pos0()

Master logic for setting pos0

get_pos0_from_dict()

Initialize the starting points from a passed dictionary.

The pos0 passed to the Sampler should be a dictionary with keys matching the search_parameter_keys. Each entry should have shape (ntemps, nwalkers).

get_pos0_from_minimize(minimize_list=None)

Draw the initial positions using an initial minimization step

See pos0 in the class initialization for details.

Returns:

pos0: list

The initial postitions of the walkers, with shape (ntemps, nwalkers, ndim)

get_pos0_from_prior()

Draw the initial positions from the prior

Returns:

pos0: list

The initial postitions of the walkers, with shape (ntemps, nwalkers, ndim)

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(*args, **kwargs)

A template method to run in subclasses

property sampler_function_kwargs

Kwargs passed to samper.sampler()

property sampler_init_kwargs

Kwargs passed to initialize ptemcee.Sampler()

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

setup_sampler()

Either initialize the sampler or read in the resume file

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.