autofit.PySwarmsLocal

class autofit.PySwarmsLocal(name: Optional[str] = None, path_prefix: Optional[str] = None, unique_tag: Optional[str] = None, prior_passer=None, iterations_per_update: int = None, number_of_cores: int = None, session: Optional[sqlalchemy.orm.session.Session] = None, **kwargs)
__init__(name: Optional[str] = None, path_prefix: Optional[str] = None, unique_tag: Optional[str] = None, prior_passer=None, iterations_per_update: int = None, number_of_cores: int = None, session: Optional[sqlalchemy.orm.session.Session] = None, **kwargs)

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.
  • prior_passer – Controls how priors are passed from the results of this NonLinearSearch to a subsequent non-linear search.
  • initializer – Generates the initialize samples of non-linear parameter space (see autofit.non_linear.initializer).
  • number_of_cores (int) – The number of cores Emcee sampling is performed using a Python multiprocessing Pool instance. If 1, a pool instance is not created and the job runs in serial.

Methods

__init__(name, path_prefix, unique_tag[, …]) A PySwarms Particle Swarm Optimizer global non-linear search.
copy_with_paths(paths)
fit(model, analysis[, info, pickle_files, …]) 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.
fitness_function_from_model_and_analysis(…)
make_pool() Make the pool instance used to parallelize a NonLinearSearch alongside a set of unique ids for every process in the pool.
make_sneaky_pool(fitness_function) Create a pool for multiprocessing that uses slight-of-hand to avoid copying the fitness function between processes multiple times.
optimise(factor, model_approx, status) Perform optimisation for expectation propagation.
perform_update(model, analysis, during_analysis) Perform an update of the NonLinearSearch results, which occurs every iterations_per_update of the non-linear search.
plot_results(samples)
remove_state_files()
sampler_from(model, fitness_function, …) Get the static Dynesty sampler which performs the non-linear search, passing it all associated input Dynesty variables.
samples_from(model)
samples_via_results_from(model)

Attributes

config_dict_run
config_dict_search
config_dict_settings
config_type
logger
name
paths
samples_cls
timer
sampler_from(model, fitness_function, bounds, init_pos)

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