autofit.PySwarmsGlobal#
- class PySwarmsGlobal[source]#
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.
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
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.