OptimizerMixin

class pyhf.optimize.mixins.OptimizerMixin(**kwargs)

Bases: object

Mixin Class to build optimizers.

Attributes

maxiter

verbose

Methods

__init__(**kwargs)

Create an optimizer.

Parameters

• maxiter (int) – maximum number of iterations. Default is 100000.

• verbose (int) – verbose output level during minimization. Default is off (0).

minimize(objective, data, pdf, init_pars, par_bounds, fixed_vals=None, return_fitted_val=False, return_result_obj=False, return_uncertainties=False, return_correlations=False, do_grad=None, do_stitch=False, **kwargs)

Find parameters that minimize the objective.

Parameters

• objective (func) – objective function

• data (list) – observed data

• pdf (Model) – The statistical model adhering to the schema model.json

• init_pars (list) – initial parameters

• par_bounds (list) – parameter boundaries

• fixed_vals (list) – fixed parameter values

• return_fitted_val (bool) – Return bestfit value of the objective. Default is off (False).

• return_result_obj (bool) – Return scipy.optimize.OptimizeResult. Default is off (False).

• return_uncertainties (bool) – Return uncertainties on the fitted parameters. Default is off (False).

• return_correlations (bool) – Return correlations of the fitted parameters. Default is off (False).

• do_grad (bool) – enable autodifferentiation mode. Default depends on backend (pyhf.tensorlib.default_do_grad).

• do_stitch (bool) – enable splicing/stitching fixed parameter.

• kwargs – other options to pass through to underlying minimizer

Returns

• parameters (tensor): fitted parameters

• minimum (float): if return_fitted_val flagged, return minimized objective value

• result (scipy.optimize.OptimizeResult): if return_result_obj flagged

Return type

Fitted parameters or tuple of results