#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
from itertools import groupby
from logging import Logger
from typing import Optional, Union
from ax.core.metric import Metric
from ax.core.objective import MultiObjective, Objective, ScalarizedObjective
from ax.core.outcome_constraint import (
ComparisonOp,
ObjectiveThreshold,
OutcomeConstraint,
ScalarizedOutcomeConstraint,
)
from ax.core.risk_measures import RiskMeasure
from ax.exceptions.core import UserInputError
from ax.utils.common.base import Base
from ax.utils.common.logger import get_logger
logger: Logger = get_logger(__name__)
TRefPoint = list[ObjectiveThreshold]
# Sentinels for default arguments when None is a valid input
_NO_OUTCOME_CONSTRAINTS = [
OutcomeConstraint(Metric("", lower_is_better=True), ComparisonOp.GEQ, 0)
]
_NO_OBJECTIVE_THRESHOLDS = [ObjectiveThreshold(Metric("", lower_is_better=True), 0)]
_NO_RISK_MEASURE = RiskMeasure("", {})
[docs]class OptimizationConfig(Base):
"""An optimization configuration, which comprises an objective,
outcome constraints and an optional risk measure.
There is no minimum or maximum number of outcome constraints, but an
individual metric can have at most two constraints--which is how we
represent metrics with both upper and lower bounds.
"""
def __init__(
self,
objective: Objective,
outcome_constraints: Optional[list[OutcomeConstraint]] = None,
risk_measure: Optional[RiskMeasure] = None,
) -> None:
"""Inits OptimizationConfig.
Args:
objective: Metric+direction to use for the optimization.
outcome_constraints: Constraints on metrics.
risk_measure: An optional risk measure, used for robust optimization.
Must be used with a `RobustSearchSpace`.
"""
constraints: list[OutcomeConstraint] = (
[] if outcome_constraints is None else outcome_constraints
)
self._validate_optimization_config(
objective=objective,
outcome_constraints=constraints,
risk_measure=risk_measure,
)
self._objective: Objective = objective
self._outcome_constraints: list[OutcomeConstraint] = constraints
self.risk_measure: Optional[RiskMeasure] = risk_measure
[docs] def clone(self) -> "OptimizationConfig":
"""Make a copy of this optimization config."""
return self.clone_with_args()
[docs] def clone_with_args(
self,
objective: Optional[Objective] = None,
outcome_constraints: Optional[
list[OutcomeConstraint]
] = _NO_OUTCOME_CONSTRAINTS,
risk_measure: Optional[RiskMeasure] = _NO_RISK_MEASURE,
) -> "OptimizationConfig":
"""Make a copy of this optimization config."""
objective = self.objective.clone() if objective is None else objective
outcome_constraints = (
[constraint.clone() for constraint in self.outcome_constraints]
if outcome_constraints is _NO_OUTCOME_CONSTRAINTS
else outcome_constraints
)
risk_measure = (
self.risk_measure if risk_measure is _NO_RISK_MEASURE else risk_measure
)
return OptimizationConfig(
objective=objective,
outcome_constraints=outcome_constraints,
risk_measure=risk_measure,
)
@property
def objective(self) -> Objective:
"""Get objective."""
return self._objective
@objective.setter
def objective(self, objective: Objective) -> None:
"""Set objective if not present in outcome constraints."""
self._validate_optimization_config(
objective, self.outcome_constraints, self.risk_measure
)
self._objective = objective
@property
def all_constraints(self) -> list[OutcomeConstraint]:
"""Get outcome constraints."""
return self.outcome_constraints
@property
def outcome_constraints(self) -> list[OutcomeConstraint]:
"""Get outcome constraints."""
return self._outcome_constraints
@property
def metrics(self) -> dict[str, Metric]:
constraint_metrics = {
oc.metric.name: oc.metric
for oc in self._outcome_constraints
if not isinstance(oc, ScalarizedOutcomeConstraint)
}
scalarized_constraint_metrics = {
metric.name: metric
for oc in self._outcome_constraints
if isinstance(oc, ScalarizedOutcomeConstraint)
for metric in oc.metrics
}
objective_metrics = {metric.name: metric for metric in self.objective.metrics}
return {
**constraint_metrics,
**scalarized_constraint_metrics,
**objective_metrics,
}
@property
def is_moo_problem(self) -> bool:
return self.objective is not None and isinstance(self.objective, MultiObjective)
@outcome_constraints.setter
def outcome_constraints(self, outcome_constraints: list[OutcomeConstraint]) -> None:
"""Set outcome constraints if valid, else raise."""
self._validate_optimization_config(
objective=self.objective,
outcome_constraints=outcome_constraints,
risk_measure=self.risk_measure,
)
self._outcome_constraints = outcome_constraints
@staticmethod
def _validate_optimization_config(
objective: Objective,
outcome_constraints: Optional[list[OutcomeConstraint]] = None,
risk_measure: Optional[RiskMeasure] = None,
) -> None:
"""Ensure outcome constraints are valid and the risk measure is
compatible with the objective.
Either one or two outcome constraints can reference one metric.
If there are two constraints, they must have different 'ops': one
LEQ and one GEQ.
If there are two constraints, the bound of the GEQ op must be less
than the bound of the LEQ op.
Args:
objective: Metric+direction to use for the optimization.
outcome_constraints: Constraints to validate.
risk_measure: An optional risk measure to validate.
"""
if type(objective) is MultiObjective:
# Raise error on exact equality; `ScalarizedObjective` is OK
raise ValueError(
(
"OptimizationConfig does not support MultiObjective. "
"Use MultiObjectiveOptimizationConfig instead."
)
)
outcome_constraints = outcome_constraints or []
# Only vaidate `outcome_constraints`
outcome_constraints = [
constraint
for constraint in outcome_constraints
if isinstance(constraint, ScalarizedOutcomeConstraint) is False
]
unconstrainable_metrics = objective.get_unconstrainable_metrics()
OptimizationConfig._validate_outcome_constraints(
unconstrainable_metrics=unconstrainable_metrics,
outcome_constraints=outcome_constraints,
)
@staticmethod
def _validate_outcome_constraints(
unconstrainable_metrics: list[Metric],
outcome_constraints: list[OutcomeConstraint],
) -> None:
constraint_metrics = [
constraint.metric.name for constraint in outcome_constraints
]
for metric in unconstrainable_metrics:
if metric.name in constraint_metrics:
raise ValueError("Cannot constrain on objective metric.")
def get_metric_name(oc: OutcomeConstraint) -> str:
return oc.metric.name
sorted_constraints = sorted(outcome_constraints, key=get_metric_name)
for metric_name, constraints_itr in groupby(
sorted_constraints, get_metric_name
):
constraints: list[OutcomeConstraint] = list(constraints_itr)
constraints_len = len(constraints)
if constraints_len == 2:
if constraints[0].op == constraints[1].op:
raise ValueError(f"Duplicate outcome constraints {metric_name}")
lower_bound_idx = 0 if constraints[0].op == ComparisonOp.GEQ else 1
upper_bound_idx = 1 - lower_bound_idx
lower_bound = constraints[lower_bound_idx].bound
upper_bound = constraints[upper_bound_idx].bound
if lower_bound >= upper_bound:
raise ValueError(
f"Lower bound {lower_bound} is >= upper bound "
+ f"{upper_bound} for {metric_name}"
)
elif constraints_len > 2:
raise ValueError(f"Duplicate outcome constraints {metric_name}")
def __repr__(self) -> str:
base_repr = (
f"{self.__class__.__name__}("
"objective=" + repr(self.objective) + ", "
"outcome_constraints=" + repr(self.outcome_constraints)
)
if self.risk_measure is None:
end_repr = ")"
else:
end_repr = ", risk_measure=" + repr(self.risk_measure) + ")"
return base_repr + end_repr
def __hash__(self) -> int:
"""Make the class hashable to support grouping of GeneratorRuns."""
return hash(repr(self))
[docs]class MultiObjectiveOptimizationConfig(OptimizationConfig):
"""An optimization configuration for multi-objective optimization,
which comprises multiple objective, outcome constraints, objective
thresholds, and an optional risk measure.
There is no minimum or maximum number of outcome constraints, but an
individual metric can have at most two constraints--which is how we
represent metrics with both upper and lower bounds.
ObjectiveThresholds should be present for every objective. A good
rule of thumb is to set them 10% below the minimum acceptable value
for each metric.
"""
def __init__(
self,
objective: Union[MultiObjective, ScalarizedObjective],
outcome_constraints: Optional[list[OutcomeConstraint]] = None,
objective_thresholds: Optional[list[ObjectiveThreshold]] = None,
risk_measure: Optional[RiskMeasure] = None,
) -> None:
"""Inits OptimizationConfig.
Args:
objective: Metric+direction to use for the optimization. Should be either a
MultiObjective or a ScalarizedObjective.
outcome_constraints: Constraints on metrics.
objective_thesholds: Thresholds objectives must exceed. Used for
multi-objective optimization and for calculating frontiers
and hypervolumes.
risk_measure: An optional risk measure, used for robust optimization.
Must be used with a `RobustSearchSpace`.
"""
constraints: list[OutcomeConstraint] = (
[] if outcome_constraints is None else outcome_constraints
)
objective_thresholds = objective_thresholds or []
self._validate_optimization_config(
objective=objective,
outcome_constraints=constraints,
objective_thresholds=objective_thresholds,
risk_measure=risk_measure,
)
self._objective: Union[MultiObjective, ScalarizedObjective] = objective
self._outcome_constraints: list[OutcomeConstraint] = constraints
self._objective_thresholds: list[ObjectiveThreshold] = objective_thresholds
self.risk_measure: Optional[RiskMeasure] = risk_measure
# pyre-fixme[14]: Inconsistent override.
[docs] def clone_with_args(
self,
objective: Optional[Union[MultiObjective, ScalarizedObjective]] = None,
outcome_constraints: Optional[
list[OutcomeConstraint]
] = _NO_OUTCOME_CONSTRAINTS,
objective_thresholds: Optional[
list[ObjectiveThreshold]
] = _NO_OBJECTIVE_THRESHOLDS,
risk_measure: Optional[RiskMeasure] = _NO_RISK_MEASURE,
) -> "MultiObjectiveOptimizationConfig":
"""Make a copy of this optimization config."""
objective = self.objective.clone() if objective is None else objective
outcome_constraints = (
[constraint.clone() for constraint in self.outcome_constraints]
if outcome_constraints is _NO_OUTCOME_CONSTRAINTS
else outcome_constraints
)
objective_thresholds = (
[ot.clone() for ot in self.objective_thresholds]
if objective_thresholds is _NO_OBJECTIVE_THRESHOLDS
else objective_thresholds
)
risk_measure = (
self.risk_measure if risk_measure is _NO_RISK_MEASURE else risk_measure
)
return MultiObjectiveOptimizationConfig(
objective=objective,
outcome_constraints=outcome_constraints,
objective_thresholds=objective_thresholds,
risk_measure=risk_measure,
)
@property
def objective(self) -> Union[MultiObjective, ScalarizedObjective]:
"""Get objective."""
return self._objective
@objective.setter
def objective(self, objective: Union[MultiObjective, ScalarizedObjective]) -> None:
"""Set objective if not present in outcome constraints."""
self._validate_optimization_config(
objective=objective,
outcome_constraints=self.outcome_constraints,
objective_thresholds=self.objective_thresholds,
risk_measure=self.risk_measure,
)
self._objective = objective
@property
def all_constraints(self) -> list[OutcomeConstraint]:
"""Get all constraints and thresholds."""
return self.outcome_constraints + self.objective_thresholds
@property
def metrics(self) -> dict[str, Metric]:
constraint_metrics = {
oc.metric.name: oc.metric
for oc in self.all_constraints
if not isinstance(oc, ScalarizedOutcomeConstraint)
}
scalarized_constraint_metrics = {
metric.name: metric
for oc in self.all_constraints
if isinstance(oc, ScalarizedOutcomeConstraint)
for metric in oc.metrics
}
objective_metrics = {metric.name: metric for metric in self.objective.metrics}
return {
**constraint_metrics,
**scalarized_constraint_metrics,
**objective_metrics,
}
@property
def objective_thresholds(self) -> list[ObjectiveThreshold]:
"""Get objective thresholds."""
return self._objective_thresholds
@objective_thresholds.setter
def objective_thresholds(
self, objective_thresholds: list[ObjectiveThreshold]
) -> None:
"""Set outcome constraints if valid, else raise."""
self._validate_optimization_config(
objective=self.objective,
objective_thresholds=objective_thresholds,
risk_measure=self.risk_measure,
)
self._objective_thresholds = objective_thresholds
@property
def objective_thresholds_dict(self) -> dict[str, ObjectiveThreshold]:
"""Get a mapping from objective metric name to the corresponding
threshold.
"""
return {ot.metric.name: ot for ot in self._objective_thresholds}
@staticmethod
def _validate_optimization_config(
objective: Objective,
outcome_constraints: Optional[list[OutcomeConstraint]] = None,
objective_thresholds: Optional[list[ObjectiveThreshold]] = None,
risk_measure: Optional[RiskMeasure] = None,
) -> None:
"""Ensure outcome constraints are valid and the risk measure is
compatible with the objective.
Either one or two outcome constraints can reference one metric.
If there are two constraints, they must have different 'ops': one
LEQ and one GEQ.
If there are two constraints, the bound of the GEQ op must be less
than the bound of the LEQ op.
Args:
objective: Metric+direction to use for the optimization.
outcome_constraints: Constraints to validate.
objective_thesholds: Thresholds objectives must exceed.
risk_measure: An optional risk measure to validate.
"""
if not isinstance(objective, (MultiObjective, ScalarizedObjective)):
raise TypeError(
(
"`MultiObjectiveOptimizationConfig` requires an objective "
"of type `MultiObjective` or `ScalarizedObjective`. "
"Use `OptimizationConfig` instead if using a "
"single-metric objective."
)
)
outcome_constraints = outcome_constraints or []
objective_thresholds = objective_thresholds or []
if isinstance(objective, MultiObjective):
objectives_by_name = {obj.metric.name: obj for obj in objective.objectives}
check_objective_thresholds_match_objectives(
objectives_by_name=objectives_by_name,
objective_thresholds=objective_thresholds,
)
unconstrainable_metrics = objective.get_unconstrainable_metrics()
OptimizationConfig._validate_outcome_constraints(
unconstrainable_metrics=unconstrainable_metrics,
outcome_constraints=outcome_constraints,
)
def __repr__(self) -> str:
base_repr = (
f"{self.__class__.__name__}("
"objective=" + repr(self.objective) + ", "
"outcome_constraints=" + repr(self.outcome_constraints) + ", "
"objective_thresholds=" + repr(self.objective_thresholds)
)
if self.risk_measure is None:
end_repr = ")"
else:
end_repr = ", risk_measure=" + repr(self.risk_measure) + ")"
return base_repr + end_repr
[docs]def check_objective_thresholds_match_objectives(
objectives_by_name: dict[str, Objective],
objective_thresholds: list[ObjectiveThreshold],
) -> None:
"""Error if thresholds on objective_metrics bound from the wrong direction or
if there is a mismatch between objective thresholds and objectives.
"""
obj_thresh_metrics = set()
for threshold in objective_thresholds:
metric_name = threshold.metric.name
if metric_name not in objectives_by_name:
raise UserInputError(
f"Objective threshold {threshold} is on metric '{metric_name}', "
f"but that metric is not among the objectives."
)
if metric_name in obj_thresh_metrics:
raise UserInputError(
"More than one objective threshold specified for metric "
f"{metric_name}."
)
obj_thresh_metrics.add(metric_name)
minimize = objectives_by_name[metric_name].minimize
bounded_above = threshold.op == ComparisonOp.LEQ
is_aligned = minimize == bounded_above
if not is_aligned:
raise UserInputError(
f"Objective threshold on {metric_name} bounds from "
f"{'above' if bounded_above else 'below'} "
f"but {metric_name} is being "
f"{'minimized' if minimize else 'maximized'}."
)
