Passed Predict Params to RAPS for conformity score calculation and EnsembleClassifier

[VishnoiAman777]

Description

This change adds a test case to ensure that predict_params are correctly passed and handled when using Ensemble Classifier to predict_proba function for an estimator and RAPSConformityScore Class. Addtional test test_raps_with_predict_params is added to verify that the predict_params are properly passed through the fit and predict methods in RAPSConformityScore.

Fixes #614

Type of change

• Bug fix (non-breaking change which fixes an issue)

How Has This Been Tested?

The following test was added to verify the changes:

• test_raps_with_predict_params
  • This test verifies that predict_params are correctly passed through the fit and predict methods when using RAPSConformityScore

Checklist

• I have read the contributing guidelines
• I have updated the HISTORY.rst and AUTHORS.rst files
• Linting passes successfully: make lint
• Typing passes successfully: make type-check
• Unit tests pass successfully: make tests
• Coverage is 100%: make coverage
• When updating documentation: doc builds successfully and without warnings: make doc
• When updating documentation: code examples in doc run successfully: make doctest

[allglc]

Hello @VishnoiAman777, thank you for the contribution!

It seems that indeed predict_params were not passed to the estimator in predict_proba_calib for the "prefit" case. Good catch!

predict_params were also not passed to the estimator in RAPSConformityScore. Do you think it is the only score where the issue happens? I think it is but I would like your opinion.

I have made comments on some of your modifications, please look at them when you have the time.

Otherwise, I was wondering about the possible uses of predict_params. Is it only used by gradient boosting models? Are you aware of another use?

[VishnoiAman777]

Hi @allglc,
Thank you for your response.

The RAPSConformityScore is the only case where we require predict_params. This is because of k_star and lambda required in RAPS. As far as I can tell from reviewing the codebase, this requirement does not apply elsewhere.

As you have said predict_params is typically needed in Boosting implementations, many third-party libraries—such as XGBoost and CatBoost—also support them. My rationale for including them is to ensure compatibility with any estimator that follows the scikit-learn convention. This also allows for their use in custom estimators and within scikit-learn pipeline objects.

Additionally, I am not able to see any comments on my changes yet as you have mentioned. Have you started a code review?