autofit.BFGS#

class autofit.BFGS(name: str | None = None, path_prefix: str | None = None, unique_tag: str | None = None, tol: float | None = None, disp: bool = False, eps: float = 1e-08, ftol: float = 2.220446049250313e-09, gtol: float = 1e-05, iprint: float = -1.0, maxcor: int = 10, maxfun: int = 15000, maxiter: int = 15000, maxls: int = 20, initializer: AbstractInitializer | None = None, iterations_per_full_update: int = None, iterations_per_quick_update: int = None, silence: bool = False, session: Session | None = None, **kwargs)[source]#

Bases: AbstractBFGS

The BFGS non-linear search, which wraps the scipy Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm.

For a full description of the scipy BFGS method, checkout its documentation:

https://docs.scipy.org/doc/scipy/reference/optimize.minimize-bfgs.html#optimize-minimize-bfgs

Abstract wrapper for the BFGS and L-BFGS scipy non-linear searches.

Parameters:

tol – Tolerance for termination.
disp – Set to True to print convergence messages.
maxiter – Maximum number of iterations.
maxfun – Maximum number of function evaluations.

Methods

`apply_test_mode`	Override in subclasses to reduce sampler iterations for test mode.
`check_model`
`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.
`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.
`output_search_internal`
`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`
`plot_start_point`	Visualize the starting point of the non-linear search, using an instance of the model at the starting point of the maximum likelihood estimator.
`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.
`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_internal_from`	Returns a Samples object from the LBFGS internal results.
`start_resume_fit`

Attributes

`logger`	Log 'msg % args' with severity 'DEBUG'.
`method`
`name`
`options`
`paths`
`should_plot_start_point`
`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.