fitAlgs.basinhopping module

Author:Dominic Hunt
class fitAlgs.basinhopping.Basinhopping(method=None, number_start_points=4, allow_boundary_fits=True, boundary_fit_sensitivity=5, **kwargs)[source]

Bases: fitAlgs.fitAlg.FitAlg

The class for fitting data using scipy.optimise.basinhopping

Parameters:
  • fit_sim (fitAlgs.fitSims.FitSim instance, optional) – An instance of one of the fitting simulation methods. Default fitAlgs.fitSims.FitSim
  • fit_measure (string, optional) – The name of the function used to calculate the quality of the fit. The value it returns provides the fitter with its fitting guide. Default -loge
  • fit_measure_args (dict, optional) – The parameters used to initialise fit_measure and extra_fit_measures. Default None
  • extra_fit_measures (list of strings, optional) – List of fit measures not used to fit the model, but to provide more information. Any arguments needed for these measures should be placed in fit_measure_args. Default None
  • bounds (dictionary of tuples of length two with floats, optional) – The boundaries for methods that use bounds. If unbounded methods are specified then the bounds will be ignored. Default is None, which translates to boundaries of (0, np.inf) for each parameter.
  • boundary_excess_cost (str or callable returning a function, optional) – The function is used to calculate the penalty for exceeding the boundaries. Default is boundFunc.scalarBound()
  • boundary_excess_cost_properties (dict, optional) – The parameters for the boundary_excess_cost function. Default {}
  • method (string or list of strings, optional) – The name of the fitting method or list of names of fitting methods or name of list of fitting methods. Valid names found in the notes. Default unconstrained
  • number_start_points (int, optional) – The number of starting points generated for each parameter. Default 4
  • allow_boundary_fits (bool, optional) – Defines if fits that reach a boundary should be considered the same way as those that do not. Default is True
  • boundSensitivity (int, optional) – Defines the smallest number of decimal places difference (so the minimal difference) between a fit value and its related boundaries before a fit value is considered different from a boundary. The default is 5. This is only valid if allow_boundary_fits is False
Name

The name of the fitting method

Type:string
unconstrained

The list of valid unconstrained fitting methods

Type:list
constrained

The list of valid constrained fitting methods

Type:list

Notes

unconstrained = [‘Nelder-Mead’,’Powell’,’CG’,’BFGS’] constrained = [‘L-BFGS-B’,’TNC’,’SLSQP’] Custom fitting algorithms are also allowed in theory, but it has yet to be implemented.

For each fitting function a set of different starting parameters will be tried. These are the combinations of all the values of the different parameters. For each starting parameter provided a set of number_start_points starting points will be chosen, surrounding the starting point provided. If the starting point provided is less than one it will be assumed that the values cannot exceed 1, otherwise, unless otherwise told, it will be assumed that they can take any value and will be chosen to be eavenly spaced around the provided value.

See also

fitAlgs.fitAlg.fitAlg
The general fitting method class, from which this one inherits
filtAlgs.fitSims.fitSim
The general fitting class
scipy.optimise.basinhopping
The fitting class this wraps around
callback(x, f, accept)[source]

Used for storing the state after each stage of fitter

Parameters:
  • x (coordinates of the trial minimum) –
  • f (function value of the trial minimum) –
  • accept (whether or not that minimum was accepted) –
constrained = ['L-BFGS-B', 'TNC', 'SLSQP']
fit(simulator, model_parameter_names, model_initial_parameters)[source]

Runs the model through the fitting algorithms and starting parameters and returns the best one.

Parameters:
  • simulator (function) – The function used by a fitting algorithm to generate a fit for given model parameters. One example is fitAlgs.fitAlg.fitness
  • model_parameter_names (list of strings) – The list of initial parameter names
  • model_initial_parameters (list of floats) – The list of the intial parameters
Returns:

  • best_fit_parameters (list of floats) – The best fitting parameters
  • fit_quality (float) – The quality of the fit as defined by the quality function chosen.
  • testedParams (tuple of two lists and a dictionary) – The two lists are a list containing the parameter values tested, in the order they were tested, and the fit qualities of these parameters. The dictionary contains the coordinates of the trial minimum, the function value of the trial minimum and whether or not that minimum was accepted. Each is stored in a list.

See also

fitAlgs.fitAlg.fitness()

unconstrained = ['Nelder-Mead', 'Powell', 'CG', 'BFGS']