Binary risk control - implementation batch 1

[Valentin-Laurent]

No description provided.

[Copilot]

Pull Request Overview

This PR implements binary classification risk control in its simplest form, focusing on mono-risk and unidimensional lambda using thresholding on predict_proba. The implementation includes cleanup of existing risk control code and introduction of new binary classification risk control components.

Key changes:

• Remove unnecessary components from existing LTT procedure (lambda=None check, p_values output)
• Add support for array-based n_obs in p-value calculations for binary classification scenarios
• Implement BinaryClassificationRisk class with predefined instances for precision, recall, and accuracy
• Introduce BinaryClassificationController for threshold-based binary classification risk control

Reviewed Changes

Copilot reviewed 7 out of 7 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
mapie/control_risk/ltt.py	Remove p_values return, lambda=None check, and add array support for n_obs
mapie/control_risk/p_values.py	Add array support for n_obs parameter in Hoeffding-Bentkus p-value computation
mapie/risk_control.py	Add BinaryClassificationRisk class and predefined risk instances
mapie/risk_control_draft.py	Implement BinaryClassificationController with threshold-based risk control
mapie/tests/test_control_risk.py	Update tests for LTT changes and add n_obs array testing
mapie/tests/test_risk_control.py	Add comprehensive tests for BinaryClassificationRisk instances
mapie/init.py	Remove risk_control_draft from exports

Comments suppressed due to low confidence (2)

mapie/tests/test_risk_control.py:848

• The test should verify the specific condition that leads to None result (effective_sample_size == 0) rather than using a general elif clause. Consider adding an explicit assertion that effective_sample_func returns 0 when result is None.

    elif result is None:

mapie/risk_control_draft.py:18

• The entire BinaryClassificationController class is marked with pragma: no cover, indicating missing test coverage. This class implements core functionality and should have comprehensive unit tests.

class BinaryClassificationController:  # pragma: no cover

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
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Additional details and impacted files

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