fitbenchmarking.controllers.lmfit_controller module

Implements a controller for the lmfit fitting software.

class fitbenchmarking.controllers.lmfit_controller.LmfitController(cost_func)

Bases: Controller

Controller for lmfit

algorithm_check = {'MCMC': ['emcee'], 'all': ['differential_evolution', 'powell', 'cobyla', 'slsqp', 'emcee', 'nelder', 'least_squares', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg', 'leastsq', 'newton', 'tnc', 'lbfgsb', 'bfgs', 'cg', 'ampgo', 'shgo', 'dual_annealing'], 'bfgs': ['lbfgsb', 'bfgs'], 'conjugate_gradient': ['cg', 'newton', 'powell'], 'deriv_free': ['powell', 'cobyla', 'nelder', 'differential_evolution'], 'gauss_newton': ['newton', 'tnc'], 'general': ['nelder', 'powell', 'cg', 'bfgs', 'newton', 'lbfgs', 'tnc', 'slsqp', 'differential_evolution', 'shgo', 'dual_annealing'], 'global_optimization': ['differential_evolution', 'ampgo', 'shgo', 'dual_annealing'], 'levenberg-marquardt': ['leastsq'], 'ls': ['least_squares', 'leastsq'], 'simplex': ['nelder'], 'steepest_descent': [], 'trust_region': ['least_squares', 'trust-ncg', 'trust-exact', 'trust-krylov', 'trust-constr', 'dogleg']}

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.

bounds_required_minimizers = ['dual_annealing', 'differential_evolution']

Used to check whether the selected minimizer is compatible with problems that don’t have parameter bounds

cleanup()

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

fit()

Run problem with lmfit

hessian_enabled_solvers = ['newton', 'dogleg', 'trust-constr', 'trust-ncg', 'trust-krylov', 'trust-exact']

Within the controller class, you must define the list hessian_enabled_solvers if any of the minimizers for the specific software are able to use hessian information.

  • hessian_enabled_solvers: a list of minimizers in a specific software that allow Hessian information to be passed into the fitting algorithm

jacobian_enabled_solvers = ['cg', 'bfgs', 'newton', 'lbfgsb', 'tnc', 'slsqp', 'dogleg', 'trust-ncg', 'trust-krylov', 'trust-exact']

Within the controller class, you must define the list jacobian_enabled_solvers if any of the minimizers for the specific software are able to use jacobian information.

  • jacobian_enabled_solvers: a list of minimizers in a specific software that allow Jacobian information to be passed into the fitting algorithm

lmfit_jacobians(params)

lmfit jacobians

lmfit_loglike(params)

lmfit resdiuals

lmfit_resdiuals(params)

lmfit resdiuals

setup()

Setup problem ready to be run with lmfit

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