MinuitMinimizer¶

class minkit.MinuitMinimizer(evaluator, **minimizer_config)[source]¶

Bases: minkit.Minimizer

Interface with the iminuit.Minuit class. In the calls to the different methods of this class it is ensured that the parameters of the PDF(s) from the evaluator instance have their value set to that of the minimum after a minimization process.

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

Attributes Summary

`evaluator`	Evaluator of the minimizer.
`minuit`	Underlying `iminuit.Minuit` object.

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.
`hesse`(args, *kwargs)	Arguments are forwarded to the `iminuit.Minuit.hesse()` function, and the values of the parameters are set to those from the minimization result.
`migrad`(args, *kwargs)	Arguments are forwarded to the `iminuit.Minuit.migrad()` function, and the values of the parameters are set to those from the minimization result.
`minimization_profile`(wa, values[, …])	Minimize a PDF an calculate the FCN for each set of parameters and values.
`minimize`(args, *kwargs)	Same as `MinuitMinimizer.migrad()`, but offering a common interface for all the `Minimizer` objects.
`minos`(args, *kwargs)	Arguments are forwarded to the `iminuit.Minuit.minos()` function, and the values of the parameters are set to those of the MINOS result.
`minos_profile`(args, *kwargs)	Arguments are forwarded to the `iminuit.Minuit.mnprofile()` function.
`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.

minuit¶: Underlying iminuit.Minuit object.

Methods Documentation

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

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)¶

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

hesse(*args, **kwargs)[source]¶

Arguments are forwarded to the iminuit.Minuit.hesse() function, and the values of the parameters are set to those from the minimization result.

Returns:	Output from `iminuit.Minuit.hesse()`.

migrad(*args, **kwargs)[source]¶

Arguments are forwarded to the iminuit.Minuit.migrad() function, and the values of the parameters are set to those from the minimization result.

Returns:	output from `iminuit.Minuit.migrad()`.

See also

MinuitMinimizer.minimize()

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]¶: Same as MinuitMinimizer.migrad(), but offering a common interface for all the Minimizer objects. It returns a tuple with the information whether the minimization succeded and the covariance matrix.

See also

MinuitMinimizer.migrad()

minos(*args, **kwargs)[source]¶

Arguments are forwarded to the iminuit.Minuit.minos() function, and the values of the parameters are set to those of the MINOS result. If a new minimum is found, the value of the parameter is set accordingly.

Returns:	output from `iminuit.Minuit.minos()`.