Allow outcome constraints on objective metrics in MOO (#5234)#5234
Closed
dme65 wants to merge 1 commit into
Closed
Allow outcome constraints on objective metrics in MOO (#5234)#5234dme65 wants to merge 1 commit into
dme65 wants to merge 1 commit into
Conversation
|
@dme65 has exported this pull request. If you are a Meta employee, you can view the originating Diff in D109061377. |
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #5234 +/- ##
=======================================
Coverage 96.56% 96.56%
=======================================
Files 619 619
Lines 70168 70177 +9
=======================================
+ Hits 67756 67765 +9
Misses 2412 2412 ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
|
Summary: In multi-objective optimization, the acquisition function can exploit asymmetric model uncertainty by proposing points that are extreme in easy-to-predict objectives (e.g., very small FLOPs) while hoping for favorable outcomes on uncertain objectives (e.g., NE). Users want to add constraints against the optimization direction (e.g., FLOPs >= threshold while minimizing FLOPs) to prevent exploring regions that are known to be uninteresting. Previously, Ax prevented any outcome constraint on an objective metric via `get_unconstrainable_metric_names()`. This restriction made sense for single-objective optimization (where constraining the sole objective is reducible to a constrained problem) but was overly restrictive for MOO. The downstream BoTorch acquisition functions (e.g., `qLogNoisyExpectedHypervolumeImprovement`) already support constraints on objective outputs via their `constraints` parameter, which applies a sigmoid-based feasibility indicator independently of the hypervolume objective. This diff relaxes the validation in `MultiObjectiveOptimizationConfig._validate_transformed_optimization_config()` to allow outcome constraints on objective metrics for multi-objective problems, while preserving the restriction for single-objective and scalarized-objective cases. Note: objective thresholds are insufficient for this use case because (1) they only serve as the hypervolume reference point (soft boundary, not hard constraint), and (2) they only support bounding from the direction of optimization -- you cannot set a lower bound on a minimized objective via thresholds. Reviewed By: saitcakmak Differential Revision: D109061377
|
This pull request has been merged in 2724981. |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary:
In multi-objective optimization, the acquisition function can exploit
asymmetric model uncertainty by proposing points that are extreme in
easy-to-predict objectives (e.g., very small model size) while hoping for
favorable outcomes on uncertain objectives (e.g., model accuracy). Users want to
add constraints against the optimization direction (e.g., model size >=
threshold while minimizing model size) to prevent exploring regions that
are known to be uninteresting.
Previously, Ax prevented any outcome constraint on an objective metric
via
get_unconstrainable_metric_names(). This restriction made sensefor single-objective optimization (where constraining the sole objective
is reducible to a constrained problem) but was overly restrictive for
MOO. The downstream BoTorch acquisition functions (e.g.,
qLogNoisyExpectedHypervolumeImprovement) already support constraintson objective outputs via their
constraintsparameter, which appliesa sigmoid-based feasibility indicator independently of the hypervolume
objective.
This diff relaxes the validation in
MultiObjectiveOptimizationConfig._validate_transformed_optimization_config()to allow outcome constraints on objective metrics for multi-objective
problems, while preserving the restriction for single-objective and
scalarized-objective cases.
Note: objective thresholds are insufficient for this use case because
(1) they only serve as the hypervolume reference point (soft boundary,
not hard constraint), and (2) they only support bounding from the
direction of optimization -- you cannot set a lower bound on a
minimized objective via thresholds.
Reviewed By: saitcakmak
Differential Revision: D109061377