def evaluation_function(self, *recommendations: p.Parameter) -> float:
"""Provides the evaluation crieterion for the experiment.
In case of mono-objective, it defers to evaluation_function
Otherwise, it uses the hypervolume.
This function can be overriden to provide custom behaviors.
Parameters
----------
*pareto: Parameter
pareto front provided by the optimizer
"""
if self.multiobjective_upper_bounds is None: # singleobjective case
assert len(recommendations) == 1
output = self.function(*recommendations[0].args,
**recommendations[0].kwargs)
assert isinstance(
output, numbers.Number
), f"evaluation_function can only be called on singleobjective experiments (output={output}) function={self.function}."
return output
# multiobjective case
hypervolume = mobj.HypervolumePareto(
upper_bounds=self.multiobjective_upper_bounds,
seed=self.parametrization.random_state)
for candidate in recommendations:
hypervolume.add(candidate)
return -hypervolume.best_volume
def evaluation_function(self, *recommendations: p.Parameter) -> float:
if self._pareto_size is not None and len(recommendations) > self._pareto_size:
# select a subset
hypervolume = mobj.HypervolumePareto(upper_bounds=self.multiobjective_upper_bounds)
hypervolume.extend(recommendations)
recommendations = tuple(
hypervolume.pareto_front(
size=self._pareto_size,
subset=self._pareto_subset,
subset_tentatives=self._pareto_subset_tentatives,
)
)
return min(self._evaluation.evaluation_function(recom) for recom in recommendations)
