fitbenchmarking.controllers.scipy_go_controller module
Implements a controller for the scipy fitting software. In particular, here for the scipy minimize solver for general minimization problems.
- class fitbenchmarking.controllers.scipy_go_controller.ScipyGOController(cost_func)
Bases:
fitbenchmarking.controllers.base_controller.Controller
Controller for the Scipy fitting software.
- algorithm_check = {'all': ['differential_evolution', 'shgo', 'dual_annealing'], 'bfgs': [], 'conjugate_gradient': [], 'deriv_free': ['differential_evolution'], 'gauss_newton': [], 'general': ['differential_evolution', 'shgo', 'dual_annealing'], 'global_optimization': ['differential_evolution', 'shgo', 'dual_annealing'], 'levenberg-marquardt': [], 'ls': [None], 'simplex': [], 'steepest_descent': [], 'trust_region': []}
Within the controller class, you must initialize a dictionary,
algorithm_check
, such that the keys are given by:all
- all minimizersls
- least-squares fitting algorithmsderiv_free
- derivative free algorithms (these are algorithms that cannot use information about derivatives – e.g., theSimplex
method inMantid
)general
- minimizers which solve a generic min f(x)simplex
- derivative free simplex based algorithms e.g. Nelder-Meadtrust_region
- algorithms which emply a trust region approachlevenberg-marquardt
- minimizers that use the Levenberg-Marquardt algorithmgauss_newton
- minimizers that use the Gauss Newton algorithmbfgs
- minimizers that use the BFGS algorithmconjugate_gradient
- Conjugate Gradient algorithmssteepest_descent
- Steepest Descent algorithmsglobal_optimization
- Global Optimization 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.
- cleanup()
Convert the result to a numpy array and populate the variables results will be read from.
- controller_name = 'scipy_go'
A name to be used in tables. If this is set to None it will be inferred from the class name.
- fit()
Run problem with Scipy GO.
- jacobian_enabled_solvers = ['shgo', 'dual_annealing']
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
- setup()
Setup problem ready to be run with SciPy GO