autofit.DynestyDynamic#

class DynestyDynamic[source]#

Bases: AbstractDynesty

A Dynesty non-linear search, using a dynamically changing number of live points.

For a full description of Dynesty, checkout its GitHub and readthedocs webpages:

https://github.com/joshspeagle/dynesty https://dynesty.readthedocs.io/en/latest/index.html

Parameters:

name (Optional[str]) – The name of the search, controlling the last folder results are output.
path_prefix (Optional[str]) – The path of folders prefixing the name folder where results are output.
unique_tag (Optional[str]) – The name of a unique tag for this model-fit, which will be given a unique entry in the sqlite database and also acts as the folder after the path prefix and before the search name.
nlive_init (int) – Number of live points used during the initial exploration phase.
dlogz_init (float) – Stopping criterion for the initial baseline run.
iterations_per_full_update (int) – The number of iterations performed between update (e.g. output latest model to hard-disk, visualization).
number_of_cores (int) – The number of cores sampling is performed using a Python multiprocessing Pool instance.

Methods

`apply_test_mode`	Override in subclasses to reduce sampler iterations for test mode.
`check_model`
`check_pool`
`copy_with_paths`
`exact_fit`
`fit`	Fit a model, M with some function f that takes instances of the class represented by model M and gives a score for their fitness.
`iterations_from`	Returns the next number of iterations that a dynesty call will use and the total number of iterations that have been performed so far.
`live_points_init_from`	By default, dynesty live points are generated via the sampler's in-built initialization.
`make_pool`	Make the pool instance used to parallelize a NonLinearSearch alongside a set of unique ids for every process in the pool.
`make_sneakier_pool`
`make_sneaky_pool`	Create a pool for multiprocessing that uses slight-of-hand to avoid copying the fitness function between processes multiple times.
`optimise`	Perform optimisation for expectation propagation.
`output_search_internal`
`perform_update`	Perform an update of the non-linear search's model-fitting results.
`perform_visualization`	Perform visualization of the non-linear search's model-fitting results.
`plot_results`
`plot_start_point`	Visualize the starting point of the non-linear search, using an instance of the model at the starting point of the maximum likelihood estimator.
`post_fit_output`	Cleans up the output folderds after a completed non-linear search.
`pre_fit_output`	Outputs attributes of fit before the non-linear search begins.
`read_uses_pool`	If a Dynesty fit does not use a parallel pool, and is then resumed using one, this causes significant slow down.
`result_via_completed_fit`	Returns the result of the non-linear search of a completed model-fit.
`run_search_internal`	Run the Dynesty sampler, which could be either the static of dynamic sampler.
`samples_from`	Loads the samples of a non-linear search from its output files.
`samples_info_from`
`samples_via_internal_from`	Returns a Samples object from the dynesty internal results.
`search_internal_from`	Returns an instance of the Dynesty dynamic sampler set up using the input variables of this class.
`start_resume_fit`
`write_uses_pool`	If a Dynesty fit does not use a parallel pool, and is then resumed using one, this causes significant slow down.

Attributes

`checkpoint_file`	The path to the file used for checkpointing.
`logger`	Log 'msg % args' with severity 'DEBUG'.
`name`
`number_live_points`
`paths`
`run_kwargs`	Run kwargs specific to each subclass, excluding maxcall.
`search_internal`
`search_kwargs`	Shared search kwargs passed to both Static and Dynamic Dynesty samplers.
`should_plot_start_point`
`timer`	Returns the timer of the search, which is used to output informaiton such as how long the search took and how much parallelization sped up the search time.

property run_kwargs: Dict#: Run kwargs specific to each subclass, excluding maxcall.

property search_internal#

search_internal_from(model, fitness, checkpoint_exists, pool, queue_size)[source]#

Returns an instance of the Dynesty dynamic sampler set up using the input variables of this class.

If no existing dynesty sampler exist on hard-disk (located via a checkpoint_file) a new instance is created with which sampler is performed. If one does exist, the dynesty restore() function is used to create the instance of the sampler.

Dynesty samplers with a multiprocessing pool may be created by inputting a dynesty Pool object, however non pooled instances can also be created by passing pool=None and queue_size=None.

Parameters:

model – The model which generates instances for different points in parameter space.
fitness – An instance of the fitness class used to evaluate the likelihood of each model.
pool – A dynesty Pool object which performs likelihood evaluations over multiple CPUs.
queue_size – The number of CPU’s over which multiprocessing is performed, determining how many samples are stored in the dynesty queue for samples.

property number_live_points#