Source code for ax.core.optimization_config

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-strict

from itertools import groupby
from typing import Dict, List, Optional

from ax.core.base import Base
from ax.core.metric import Metric
from ax.core.objective import Objective
from ax.core.outcome_constraint import OutcomeConstraint
from ax.core.types import ComparisonOp


MAX_OBJECTIVES: int = 4
OC_TEMPLATE: str = (
    "OptimizationConfig(objective={objective}, outcome_constraints=[{constraints}])"
)


[docs]class OptimizationConfig(Base): """An optimization configuration, which comprises an objective and outcome constraints. 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, ) -> None: """Inits OptimizationConfig. Args: objective: Metric+direction to use for the optimization. outcome_constraints: Constraints on metrics. """ constraints: List[ OutcomeConstraint ] = [] if outcome_constraints is None else outcome_constraints self._validate_optimization_config( objective=objective, outcome_constraints=constraints ) self._objective: Objective = objective self._outcome_constraints: List[OutcomeConstraint] = constraints
[docs] def clone(self) -> "OptimizationConfig": """Make a copy of this optimization config.""" return OptimizationConfig( self.objective.clone(), [constraint.clone() for constraint in self.outcome_constraints], )
@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._objective = objective @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 } objective_metrics = {metric.name: metric for metric in self.objective.metrics} return {**constraint_metrics, **objective_metrics} @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 ) self._outcome_constraints = outcome_constraints @staticmethod def _validate_optimization_config( objective: Objective, outcome_constraints: List[OutcomeConstraint] ) -> None: """Ensure outcome constraints are valid. 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: outcome_constraints: Constraints to validate. """ constraint_metrics = [ constraint.metric.name for constraint in outcome_constraints ] objective_metric_names = [metric.name for metric in objective.metrics] for name in objective_metric_names: if 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: return OC_TEMPLATE.format( objective=repr(self.objective), constraints=", ".join( constraint.__repr__() for constraint in self.outcome_constraints ), )