fitbenchmarking.controllers.bumps_controller module

Implements a controller for the Bumps fitting software.

class fitbenchmarking.controllers.bumps_controller.BumpsController(cost_func)

Bases: Controller

Controller for the Bumps fitting software.

Sasview requires a model to fit. Setup creates a model with the correct function.

algorithm_check = {'MCMC': ['dream'], 'all': ['amoeba', 'lm-bumps', 'newton', 'de', 'scipy-leastsq', 'dream'], 'bfgs': ['newton'], 'conjugate_gradient': [], 'deriv_free': ['amoeba', 'de'], 'gauss_newton': [], 'general': ['amoeba', 'newton', 'de'], 'global_optimization': ['de'], 'levenberg-marquardt': ['lm-bumps', 'scipy-leastsq'], 'ls': ['lm-bumps', 'scipy-leastsq'], 'simplex': ['amoeba'], 'steepest_descent': [], 'trust_region': ['lm-bumps', 'scipy-leastsq']}

Within the controller class, you must initialize a dictionary, algorithm_check, such that the keys are given by:

  • all - all minimizers

  • ls - least-squares fitting algorithms

  • deriv_free - derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., the Simplex method in Mantid)

  • general - minimizers which solve a generic min f(x)

  • simplex - derivative free simplex based algorithms e.g. Nelder-Mead

  • trust_region - algorithms which employ a trust region approach

  • levenberg-marquardt - minimizers that use the Levenberg-Marquardt algorithm

  • gauss_newton - minimizers that use the Gauss Newton algorithm

  • bfgs - minimizers that use the BFGS algorithm

  • conjugate_gradient - Conjugate Gradient algorithms

  • steepest_descent - Steepest Descent algorithms

  • global_optimization - Global Optimization algorithms

  • MCMC - Markov Chain Monte Carlo algorithms

The values of the dictionary are given as a list of minimizers for that specific controller that fit into each of the above categories. See for example the GSL controller.

The algorithm_check dictionary is used to determine which minimizers to run given the algorithm_type selected in Fitting Options. For guidance on how to categorise minimizers, see the Optimization Algorithms section of the FitBenchmarking docs.

cleanup()

Convert the result to a numpy array and populate the variables results will be read from.

fit()

Run problem with Bumps.

setup()

Setup problem ready to run with Bumps.

Creates a FitProblem for calling in the fit() function of Bumps

support_for_bounds = True

Used to check whether the fitting software has support for bounded problems, set as True if at least some minimizers in the fitting software have support for bounds