"""Class that provides the function to the optimization algorithm
:author: Michel Bierlaire
:date: Wed Nov 30 10:17:26 2022
"""
from biogeme_optimization.function import FunctionToMinimize, FunctionData
import biogeme.exceptions as excep
[docs]
class NegativeLikelihood(FunctionToMinimize):
"""Provides the value of the function to be minimized, as well as its
derivatives. To be used by the opimization package.
"""
# pylint: disable=too-many-instance-attributes
[docs]
def __init__(self, dimension, like, like_deriv, parameters=None):
"""Constructor"""
tolerance = None
steptol = None
if parameters is not None:
if 'tolerance' in parameters:
tolerance = parameters['tolerance']
if 'steptol' in parameters:
steptol = parameters['steptol']
super().__init__(epsilon=tolerance, steptol=steptol)
self.the_dimension = dimension #: number of parameters to estimate
self.like = like #: function calculating the log likelihood
self.like_deriv = like_deriv
"""function calculating the log likelihood and its derivatives.
"""
[docs]
def dimension(self):
"""Provides the number of variables of the problem"""
return self.the_dimension
def _f(self):
if self.x is None:
raise excep.BiogemeError('The variables must be set first.')
return -self.like(self.x, scaled=False, batch=None)
def _f_g(self):
if self.x is None:
raise excep.BiogemeError('The variables must be set first.')
f, g, *_ = self.like_deriv(
self.x, scaled=False, hessian=False, bhhh=False, batch=None
)
return FunctionData(
function=-f,
gradient=-g,
hessian=None,
)
def _f_g_h(self):
if self.x is None:
raise excep.BiogemeError('The variables must be set first.')
f, g, h, _ = self.like_deriv(
self.x, scaled=False, hessian=True, bhhh=False, batch=None
)
return FunctionData(
function=-f,
gradient=-g,
hessian=-h,
)