Validate calibration probability and outcome domains - #342
Merged
Conversation
uriahf
force-pushed
the
fix/calibration-input-validation
branch
from
August 20, 2026 12:22
7c0d49d to
f1936a4
Compare
Contributor
|
uriahf
marked this pull request as ready for review
August 20, 2026 12:35
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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
[0, 1]{0, 1}{0, 1, 2}R parity
The R
create_calibration_curve()explicitly callscheck_probs_input(). Python calibration used a separate data path and did not inherit the performance-data validation added in #341.Audit classification
Input-validation / correctness gap. Invalid calibration inputs could reach histogram/smoothing/AJ internals rather than failing at the public API boundary.
No valid-input calibration calculations, smoothing methods, censoring/competing-risk heuristics, plotting behavior, or dependencies change.