autofit.PySwarmsGlobal

class PySwarmsGlobal

Bases: AbstractPySwarms

A PySwarms Particle Swarm Optimizer global non-linear search.

For a full description of PySwarms, checkout its Github and readthedocs webpages:

https://github.com/ljvmiranda921/pyswarms

https://pyswarms.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.

• initializer (Optional[AbstractInitializer]) – Generates the initialize samples of non-linear parameter space (see autofit.non_linear.initializer).

• number_of_cores (Optional[int]) – The number of cores sampling is performed using a Python multiprocessing Pool instance.

Methods

check_model
config_dict_with_test_mode_settings_from
copy_with_paths
exact_fit	rtype Tuple[MeanField, Status]
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.
fit_sequential	Fit multiple analyses contained within the analysis sequentially.
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	rtype SneakierPool
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.
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
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.
remove_state_files
result_via_completed_fit	Returns the result of the non-linear search of a completed model-fit.
sampler_from	Get the static Dynesty sampler which performs the non-linear search, passing it all associated input Dynesty variables.
samples_from	Loads the samples of a non-linear search from its output files.
samples_via_csv_from	Returns a Samples object from the samples.csv and samples_info.json files.
samples_via_internal_from	Returns a Samples object from the pyswarms internal results.
start_resume_fit	Start a non-linear search from scratch, or resumes one which was previously terminated mid-way through.

Attributes

config_dict_run	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
config_dict_search	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
config_dict_settings	rtype Dict
config_type
logger	Log 'msg % args' with severity 'DEBUG'.
name
paths	rtype Optional[AbstractPaths]
samples_cls
timer
using_mpi	Whether the search is being performing using MPI for parallelisation or not.

sampler_from(model, fitness, bounds, init_pos)

Get the static Dynesty sampler which performs the non-linear search, passing it all associated input Dynesty variables.