Source code for ax.core.objective
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from typing import Iterable, List, Optional, Tuple
from ax.core.base import Base
from ax.core.metric import Metric
[docs]class Objective(Base):
"""Base class for representing an objective.
Attributes:
minimize: If True, minimize metric.
"""
def __init__(self, metric: Metric, minimize: bool = False) -> None:
"""Create a new objective.
Args:
metric: The metric to be optimized.
minimize: If True, minimize metric.
"""
self._metric = metric
self.minimize = minimize
@property
def metric(self) -> Metric:
"""Get the objective metric."""
return self._metric
@property
def metrics(self) -> List[Metric]:
"""Get a list of objective metrics."""
return [self._metric]
[docs] def clone(self) -> "Objective":
"""Create a copy of the objective."""
return Objective(self.metric.clone(), self.minimize)
def __repr__(self) -> str:
return 'Objective(metric_name="{}", minimize={})'.format(
self.metric.name, self.minimize
)
[docs]class ScalarizedObjective(Objective):
"""Class for an objective composed of a linear scalarization of metrics.
Attributes:
metrics: List of metrics.
weights: Weights for scalarization; default to 1.
"""
weights: List[float]
def __init__(
self,
metrics: List[Metric],
weights: Optional[List[float]] = None,
minimize: bool = False,
) -> None:
"""Create a new objective.
Args:
metric: The metric to be optimized.
weights: The weights for the linear combination of metrics.
minimize: If true, minimize the linear combination.
"""
if weights is None:
weights = [1.0 for i in range(len(metrics))]
else:
if len(weights) != len(metrics):
raise ValueError("Length of weights must equal length of metrics")
self._metrics = metrics
self.minimize = minimize
self.weights = weights
@property
def metric(self) -> Metric:
"""Override base method to error."""
raise NotImplementedError("ScalarizedObjective is composed of multiple metrics")
@property
def metrics(self) -> List[Metric]:
"""Get the objective metrics."""
return self._metrics
@property
def metric_weights(self) -> Iterable[Tuple[Metric, float]]:
"""Get the objective metrics and weights."""
return zip(self.metrics, self.weights)
[docs] def clone(self) -> "Objective":
"""Create a copy of the objective."""
return ScalarizedObjective(
metrics=[m.clone() for m in self.metrics],
weights=self.weights.copy(),
minimize=self.minimize,
)