autofit.PySwarmsGlobal

class autofit.PySwarmsGlobal(name: Optional[str] = None, path_prefix: Optional[str] = None, unique_tag: Optional[str] = None, initializer: Optional[AbstractInitializer] = None, iterations_per_update: Optional[int] = None, number_of_cores: Optional[int] = None, session: Optional[Session] = None, **kwargs)

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 – The name of the search, controlling the last folder results are output.

• path_prefix – The path of folders prefixing the name folder where results are output.

• unique_tag – 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 – Generates the initialize samples of non-linear parameter space (see autofit.non_linear.initializer).

• number_of_cores – 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
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
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.
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.
search_internal_from	Get the static Dynesty sampler which performs the non-linear search, passing it all associated input Dynesty variables.
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
config_type
logger	Log 'msg % args' with severity 'DEBUG'.
name
paths
samples_cls
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.
using_mpi	Whether the search is being performing using MPI for parallelisation or not.

search_internal_from(model, fitness, bounds, init_pos)

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