Source code for autofit.non_linear.optimize.pyswarms.local

from typing import Optional

from autofit.database.sqlalchemy_ import sa
from autofit.non_linear.optimize.pyswarms.abstract import AbstractPySwarms

[docs]class PySwarmsLocal(AbstractPySwarms): __identifier_fields__ = ( "n_particles", "cognitive", "social", "inertia", "number_of_k_neighbors", "minkowski_p_norm" )
[docs] def __init__( self, 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[sa.orm.Session] = None, **kwargs ): """ A PySwarms Particle Swarm Optimizer global non-linear search. For a full description of PySwarms, checkout its Github and readthedocs webpages: 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 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. """ super().__init__( name=name, path_prefix=path_prefix, unique_tag=unique_tag, prior_passer=prior_passer, iterations_per_update=iterations_per_update, number_of_cores=number_of_cores, session=session, **kwargs ) self.logger.debug("Creating PySwarms Search")
def sampler_from(self, model, fitness_function, bounds, init_pos): """ Get the static Dynesty sampler which performs the non-linear search, passing it all associated input Dynesty variables. """ import pyswarms options = { "c1": self.config_dict_search["cognitive"], "c2": self.config_dict_search["social"], "w": self.config_dict_search["inertia"], "k": self.config_dict_search["number_of_k_neighbors"], "p": self.config_dict_search["minkowski_p_norm"], } config_dict = self.config_dict_search config_dict.pop("cognitive") config_dict.pop("social") config_dict.pop("inertia") config_dict.pop("number_of_k_neighbors") config_dict.pop("minkowski_p_norm") return pyswarms.local_best.LocalBestPSO( dimensions=model.prior_count, bounds=bounds, init_pos=init_pos, options=options, **config_dict )