SciPyMinimizer¶

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

Bases: minkit.Minimizer

Interface with the scipy.optimize.minimize() function.

Parameters:	method (str) – method parsed by `scipy.optimize.minimize()`. evaluator (UnbinnedEvaluator, BinnedEvaluator or SimultaneousEvaluator) – evaluator to be used in the minimization. minimizer_config (dict) – configuration for the minimizer.
Raises:	ValueError – If the minimization method is unknown.

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.
`scipy_minimize`([tol, hessian_opts])	Minimize the PDF.
`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)¶

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

minimization_profile(wa, values, minimization_results=False, minimizer_config=None)¶

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. It returns a tuple with the information whether the minimization succeded and the covariance matrix.

See also

SciPyMinimizer.scipy_minimize()

restoring_state()¶: Method to ensure that modifications of parameters within a minimizer context are reset properly.

See also

MinuitMinimizer.restoring_state(), SciPyMinimizer.restoring_state()

scipy_minimize(tol=None, hessian_opts=None)[source]¶

Minimize the PDF.

Parameters:	tol (float or None) – tolerance to be used in the minimization. hessian_opts (dict or None) – options to be passed to `numdifftools.core.Hessian`.
Returns:	Result of the minimization and covariance matrix.
Return type:	scipy.optimize.OptimizeResult, numpy.ndarray