Remove Sparsity from metrics logging #124
Merged
Conversation
This file contains 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Outdated suggestions cannot be applied.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.
Sparsity has been used to characterize the diversity of the PFs and compare algorithms.
We noticed in a few papers that this metric actually does not characterize what we want because it computes the indicator only based on the points identified by the algorithm this means that an algorithm finding one or more very close points will have a low sparsity (which is supposed to be good, but it's not).
Instead, we should have a metric that computes diversity over the full objective space. But this requires extreme points for each environment I think. We welcome contributions of good diversity metrics if that exists.