FIX Add error when LeaveOneOut used in CalibratedClassifierCV
#29545
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LeaveOneOut used in CalibratedClassifierCV
| assert np.all(proba[:, :i] > 0) | ||
| assert np.all(proba[:, i + 1 :] > 0) | ||
| else: | ||
| # Check `proba` are all 1/n_classes |
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Note proba was 1/n_classes here because the original test data was unique (consisted of 10 samples belonging to 10 classes) and this was not really related to train subset not containing all classes.
I think the estimator ended up being overfit and the calibrator did not respond well to low predict values, calibrating them all to the same value. Note proba was the same value even before normalization of the probabilities
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I wonder if we even have to check ensemble=False as we use cross_val_predict to get the predictions to use to calibrate and at predict, only one estimator is fit using all the data.
sklearn/calibration.py
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| if isinstance(self.cv, LeaveOneOut): | ||
| raise ValueError( | ||
| "LeaveOneOut cross-validation does not allow" | ||
| "all classes to be present in test splits." |
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Let's be explicit by asking people to use an alternative cross-validation strategies.
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Amended. Hopefully not too long now?
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| @pytest.mark.parametrize("ensemble", [True, False]) | ||
| def test_calibration_less_classes(ensemble): | ||
| # Test to check calibration works fine when train set in a test-train |
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This is a use case where I'm really wondering if this is valid :). But this is here so let's go with it.
glemaitre
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Regarding the scope of the PR, I'm happy to already have this in the codebase. I open a related PR regarding the cross-validation and the fact that we can get a subset of classes in training. I'm not sure that this is something that we should allow but it requires much more discussions and I might overlook some aspects.
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I'll add this PR in the milestone for 1.5.2 |
Reference Issues/PRs
closes #29000
What does this implement/fix? Explain your changes.
LeaveOneOutused inCalibratedClassifierCVLeaveOneOutAny other comments?