Minimizer

class minkit.Minimizer(evaluator)[source]

Bases: object

Abstract class to serve as an API between MinKit and the different minimization methods.

Parameters:evaluator (UnbinnedEvaluator, BinnedEvaluator or SimultaneousEvaluator) – evaluator to be used in the minimization.

Attributes Summary

evaluator Evaluator of the minimizer.

Methods Summary

asymmetric_errors(name[, sigma, atol, rtol, …]) Calculate the asymmetric errors for the given parameter.
fcn_profile(wa, values) Evaluate the profile of an FCN for a set of parameters and values.
minimization_profile(wa, values[, …]) Minimize a PDF an calculate the FCN for each set of parameters and values.
minimize(*args, **kwargs) Minimize the FCN for the stored PDF and data sample.
restoring_state() Method to ensure that modifications of parameters within a minimizer context are reset properly.
set_parameter_state(name[, value, error, fixed]) Method to ensure that a modification of a parameter within a minimizer context is treated properly.

Attributes Documentation

evaluator

Evaluator of the minimizer.

Methods Documentation

asymmetric_errors(name, sigma=1, atol=1e-08, rtol=1e-05, max_call=None)[source]

Calculate the asymmetric errors for the given parameter. This is done by subdividing the bounds of the parameter into two till the variation of the FCN is one. Unlike MINOS, this method does not treat new minima. Remember that the PDF must have been minimized before a call to this function.

Parameters:
  • name (str) – name of the parameter.
  • sigma (float) – number of standard deviations to compute.
  • atol (float) – absolute tolerance for the error.
  • rtol (float) – relative tolerance for the error.
  • max_call (int or None) – maximum number of calls to calculate each error bound.
fcn_profile(wa, values)[source]

Evaluate the profile of an FCN for a set of parameters and values.

Parameters:
  • wa (str or list(str)) – single variable or set of variables.
  • values (numpy.ndarray) – values for each parameter specified in wa.
Returns:

Profile of the FCN for the given values.
Return type:

numpy.ndarray
minimization_profile(wa, values, minimization_results=False, minimizer_config=None)[source]

Minimize a PDF an calculate the FCN for each set of parameters and values.

Parameters:
  • wa (str or list(str)) – single variable or set of variables.
  • values (numpy.ndarray) – values for each parameter specified in wa.
  • minimization_results (bool) – if set to True, then the results for each step are returned.
  • minimizer_config (dict or None) – arguments passed to Minimizer.minimize().
Returns:

Profile of the FCN for the given values.
Return type:

numpy.ndarray, (list(MinimizationResult))
minimize(*args, **kwargs)[source]

Minimize the FCN for the stored PDF and data sample. Arguments depend on the specific minimizer to use. It returns a tuple with the information whether the minimization succeded and the covariance matrix.

restoring_state()[source]

Method to ensure that modifications of parameters within a minimizer context are reset properly.

See also

MinuitMinimizer.restoring_state(), SciPyMinimizer.restoring_state()

set_parameter_state(name, value=None, error=None, fixed=None)[source]

Method to ensure that a modification of a parameter within a minimizer context is treated properly. Sadly, the iminuit.Minuit class is not stateless, so each time a parameter is modified it must be notified of the change.

Parameters:
  • name (str) – name of the parameter.
  • value (float or None) – new value of a parameter.
  • error (float or None) – error of the parameter.
  • fixed (bool or None) – whether to fix the parameter.