#!/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 __future__ import annotations
import logging
from collections.abc import Iterable
from typing import Optional
from ax.core.metric import Metric
from ax.core.types import ComparisonOp
from ax.utils.common.base import SortableBase
from ax.utils.common.logger import get_logger
logger: logging.Logger = get_logger(__name__)
CONSTRAINT_WARNING_MESSAGE: str = (
"Constraint on {name} appears invalid: {bound} bound on metric "
+ "for which {is_better} values are better."
)
LOWER_BOUND_MISMATCH: dict[str, str] = {"bound": "Lower", "is_better": "lower"}
UPPER_BOUND_MISMATCH: dict[str, str] = {"bound": "Upper", "is_better": "higher"}
CONSTRAINT_THRESHOLD_WARNING_MESSAGE: str = (
"Constraint threshold on {name} appears invalid: {bound} bound on metric "
+ "for which {is_better} values is better."
)
UPPER_BOUND_THRESHOLD: dict[str, str] = {"bound": "Positive", "is_better": "lower"}
LOWER_BOUND_THRESHOLD: dict[str, str] = {"bound": "Negative", "is_better": "higher"}
[docs]class OutcomeConstraint(SortableBase):
"""Base class for representing outcome constraints.
Outcome constraints may of the form metric >= bound or metric <= bound,
where the bound can be expressed as an absolute measurement or relative
to the status quo (if applicable).
Attributes:
metric: Metric to constrain.
op: Specifies whether metric should be greater or equal
to, or less than or equal to, some bound.
bound: The bound in the constraint.
relative: [default ``True``] Whether the provided bound value is relative to
some status-quo arm's metric value. If False, ``bound`` is interpreted as an
absolute number, else ``bound`` specifies percent-difference from the
observed metric value on the status-quo arm. That is, the bound's absolute
value will be ``(1 + bound/100.0) * status_quo_metric_value``. This requires
specification of a status-quo arm in ``Experiment``.
"""
def __init__(
self, metric: Metric, op: ComparisonOp, bound: float, relative: bool = True
) -> None:
self._validate_metric_constraint_op(metric=metric, op=op)
self._metric = metric
self._op = op
self.bound = bound
self.relative = relative
@property
def metric(self) -> Metric:
return self._metric
@metric.setter
def metric(self, metric: Metric) -> None:
self._validate_metric_constraint_op(metric=metric, op=self.op)
self._metric = metric
@property
def op(self) -> ComparisonOp:
return self._op
@op.setter
def op(self, op: ComparisonOp) -> None:
self._validate_metric_constraint_op(metric=self.metric, op=op)
self._op = op
[docs] def clone(self) -> OutcomeConstraint:
"""Create a copy of this OutcomeConstraint."""
return OutcomeConstraint(
metric=self.metric.clone(),
op=self.op,
bound=self.bound,
relative=self.relative,
)
@staticmethod
def _validate_metric_constraint_op(
metric: Metric, op: ComparisonOp
) -> tuple[bool, str]:
"""Ensure constraint is compatible with metric definition.
Args:
metric: Metric to constrain.
op: Specifies whether metric should be greater or equal
to, or less than or equal to, some bound. In case metric has:
- lower_is_better=True, op is interpreted as an upper bound and
should be LEQ.
- lower_is_better=False, op is interpreted as a lower bound and
should be GEQ.
Returns: A tuple consisting of
- A boolean indicating whether the constraint is valid,
- A string containing a warning message if the constraint is invalid.
"""
fmt_data = None
if metric.lower_is_better is not None:
if op == ComparisonOp.GEQ and metric.lower_is_better:
fmt_data = LOWER_BOUND_MISMATCH
if op == ComparisonOp.LEQ and not metric.lower_is_better:
fmt_data = UPPER_BOUND_MISMATCH
if fmt_data is not None:
fmt_data["name"] = metric.name
msg = CONSTRAINT_WARNING_MESSAGE.format(**fmt_data)
logger.debug(msg)
return False, msg
return True, str()
def _validate_constraint(self) -> tuple[bool, str]:
"""Ensure constraint is compatible with metric definition.
In case metric has:
- lower_is_better=True: op is interpreted as an upper bound
and should be LEQ; bound should be positive in case
of relative constraint.
- lower_is_better=False, op is interpreted as a lower bound
and should be GEQ; bound should be negative in case
of relative constraint.
Returns: A tuple consisting of
- A boolean indicating whether the constraint is valid,
- A string containing a warning message if the constraint is invalid.
"""
valid_op, msg = self._validate_metric_constraint_op(
metric=self.metric, op=self.op
)
if not valid_op:
return False, msg
if not self.relative:
return True, str()
fmt_data = None
if self.metric.lower_is_better is not None:
if self.bound < 0 and self.metric.lower_is_better:
fmt_data = UPPER_BOUND_THRESHOLD
if self.bound > 0 and not self.metric.lower_is_better:
fmt_data = LOWER_BOUND_THRESHOLD
if fmt_data is not None:
fmt_data["name"] = self.metric.name
msg += CONSTRAINT_THRESHOLD_WARNING_MESSAGE.format(**fmt_data)
logger.debug(msg)
return False, msg
return True, str()
def __repr__(self) -> str:
op = ">=" if self.op == ComparisonOp.GEQ else "<="
relative = "%" if self.relative else ""
return f"OutcomeConstraint({self.metric.name} {op} {self.bound}{relative})"
@property
def _unique_id(self) -> str:
return str(self)
[docs]class ObjectiveThreshold(OutcomeConstraint):
"""Class for representing Objective Thresholds.
An objective threshold represents the threshold for an objective metric
to contribute to hypervolume calculations. A list containing the objective
threshold for each metric collectively form a reference point.
Objective thresholds may bound the metric from above or from below.
The bound can be expressed as an absolute measurement or relative
to the status quo (if applicable).
The direction of the bound is inferred from the Metric's lower_is_better attribute.
Attributes:
metric: Metric to constrain.
bound: The bound in the constraint.
relative: Whether you want to bound on an absolute or relative
scale. If relative, bound is the acceptable percent change.
op: automatically inferred, but manually overwritable.
specifies whether metric should be greater or equal to, or less
than or equal to, some bound.
"""
def __init__(
self,
metric: Metric,
bound: float,
relative: bool = True,
op: Optional[ComparisonOp] = None,
) -> None:
if metric.lower_is_better is None and op is None:
raise ValueError(
(
f"Metric {metric} must have attribute `lower_is_better` set or "
f"op {op} must be manually specified."
)
)
elif op is None:
op = ComparisonOp.LEQ if metric.lower_is_better else ComparisonOp.GEQ
# It's likely that the metric passed into the ObjectiveThreshold constructor
# is the same instance as the metric in the Objective. Thus, we have to clone
# the metric passed in here to ensure a 1:1 relationship between user-facing
# objects and DB objects.
super().__init__(metric=metric.clone(), op=op, bound=bound, relative=relative)
[docs] def clone(self) -> ObjectiveThreshold:
"""Create a copy of this ObjectiveThreshold."""
return ObjectiveThreshold(
metric=self.metric.clone(),
bound=self.bound,
relative=self.relative,
op=self.op,
)
def __repr__(self) -> str:
op = ">=" if self.op == ComparisonOp.GEQ else "<="
relative = "%" if self.relative else ""
return f"ObjectiveThreshold({self.metric.name} {op} {self.bound}{relative})"
[docs]class ScalarizedOutcomeConstraint(OutcomeConstraint):
"""Class for presenting outcome constraints composed of a linear
scalarization of metrics.
Attributes:
metrics: List of metrics.
weights: Weights for scalarization; default to 1.0 / len(metrics).
op: Specifies whether metric should be greater or equal
to, or less than or equal to, some bound.
bound: The bound in the constraint.
relative: [default ``True``] Whether the provided bound value is relative to
some status-quo arm's metric value. If False, ``bound`` is interpreted as an
absolute number, else ``bound`` specifies percent-difference from the
observed metric value on the status-quo arm. That is, the bound's absolute
value will be ``(1 + bound/100.0) * status_quo_metric_value``. This requires
specification of a status-quo arm in ``Experiment``.
"""
weights: list[float]
def __init__(
self,
metrics: list[Metric],
op: ComparisonOp,
bound: float,
relative: bool = True,
weights: Optional[list[float]] = None,
) -> None:
for metric in metrics:
self._validate_metric_constraint_op(metric=metric, op=op)
if weights is None:
weights = [1.0 / len(metrics)] * len(metrics)
elif len(weights) != len(metrics):
raise ValueError("Length of weights must equal length of metrics")
self._metrics = metrics
self.weights = weights
self._op = op
self.bound = bound
self.relative = relative
@property
def metric_weights(self) -> Iterable[tuple[Metric, float]]:
"""Get the objective metrics and weights."""
return zip(self.metrics, self.weights)
@property
def metrics(self) -> list[Metric]:
return self._metrics
@metrics.setter
def metrics(self, metrics: list[Metric]) -> None:
for metric in metrics:
self._validate_metric_constraint_op(metric=metric, op=self.op)
self._metrics = metrics
@property
def metric(self) -> Metric:
"""Override base method to error."""
raise NotImplementedError(
f"{type(self).__name__} is composed of multiple metrics"
)
@metric.setter
def metric(self, metric: Metric) -> None:
"""Override base method to error."""
raise NotImplementedError(
f"{type(self).__name__} is composed of multiple metrics"
)
@property
def op(self) -> ComparisonOp:
return self._op
@op.setter
def op(self, op: ComparisonOp) -> None:
for metric in self.metrics:
self._validate_metric_constraint_op(metric=metric, op=op)
self._op = op
[docs] def clone(self) -> ScalarizedOutcomeConstraint:
"""Create a copy of this ScalarizedOutcomeConstraint."""
return ScalarizedOutcomeConstraint(
metrics=[m.clone() for m in self.metrics],
op=self.op,
bound=self.bound,
relative=self.relative,
weights=self.weights.copy(),
)
def __repr__(self) -> str:
op = ">=" if self.op == ComparisonOp.GEQ else "<="
relative = "%" if self.relative else ""
return (
"ScalarizedOutcomeConstraint(metric_names={}, weights={}, {} {}{})".format(
[metric.name for metric in self.metrics],
self.weights,
op,
self.bound,
relative,
)
)
