"""SciPy Optimizer Class."""
from pyhf import exceptions
from pyhf.optimize.mixins import OptimizerMixin
import scipy
class scipy_optimizer(OptimizerMixin):
"""
Optimizer that uses :func:`scipy.optimize.minimize`.
"""
__slots__ = ['name', 'tolerance', 'solver_options']
[docs]
def __init__(self, *args, **kwargs):
"""
Initialize the scipy_optimizer.
See :class:`pyhf.optimize.mixins.OptimizerMixin` for other configuration options.
Args:
tolerance (:obj:`float`): Tolerance for termination.
See specific optimizer for detailed meaning.
Default is ``None``.
solver_options (:obj:`dict`): additional solver options. See
:func:`scipy.optimize.show_options` for additional options of
optimization solvers.
"""
self.name = 'scipy'
self.tolerance = kwargs.pop('tolerance', None)
self.solver_options = kwargs.pop('solver_options', {})
super().__init__(*args, **kwargs)
[docs]
def _get_minimizer(
self,
objective_and_grad,
init_pars,
init_bounds,
fixed_vals=None,
do_grad=False,
par_names=None,
):
return scipy.optimize.minimize
[docs]
def _minimize(
self,
minimizer,
func,
x0,
do_grad=False,
bounds=None,
fixed_vals=None,
options={},
):
"""
Same signature as :func:`scipy.optimize.minimize`.
Minimizer Options:
* maxiter (:obj:`int`): Maximum number of iterations. Default is ``100000``.
* verbose (:obj:`bool`): Print verbose output during minimization.
Default is ``False``.
* method (:obj:`str`): Minimization routine. Default is ``'SLSQP'``.
* tolerance (:obj:`float`): Tolerance for termination. See specific optimizer
for detailed meaning.
Default is ``None``.
* solver_options (:obj:`dict`): additional solver options. See
:func:`scipy.optimize.show_options` for additional options of
optimization solvers.
Returns:
fitresult (scipy.optimize.OptimizeResult): the fit result
"""
maxiter = options.pop('maxiter', self.maxiter)
verbose = options.pop('verbose', self.verbose)
method = options.pop('method', 'SLSQP')
tolerance = options.pop('tolerance', self.tolerance)
solver_options = options.pop('solver_options', self.solver_options)
if options:
raise exceptions.Unsupported(
f"Unsupported options were passed in: {list(options)}."
)
fixed_vals = fixed_vals or []
indices = [i for i, _ in fixed_vals]
values = [v for _, v in fixed_vals]
if fixed_vals:
constraints = [{'type': 'eq', 'fun': lambda v: v[indices] - values}]
# update the initial values to the fixed value for any fixed parameter
for idx, fixed_val in fixed_vals:
x0[idx] = fixed_val
else:
constraints = []
return minimizer(
func,
x0,
method=method,
jac=do_grad,
bounds=bounds,
constraints=constraints,
tol=tolerance,
options=dict(maxiter=maxiter, disp=bool(verbose), **solver_options),
)