TST Check correct interactions of `class_weight` and `sample_weight`

In scikit-learn, some estimators support class_weight and sample_weight.

It might be worth testing the correct interaction of those two types of weights, especially asserting that:

• setting a class weights to zero is equivalent to excluding the samples associated to this class from the calibration even when using non uniform sample weights.
• setting some samples weights to zero is equivalent to excluding those samples from the calibration even when if they are associated to using non uniform class weights.

---

Relevant interfaces:

• the main subclasses of sklearn.tree.BaseDecisionTree for classification, i.e.:
  • sklearn.tree.DecisionTreeClassifier
  • sklearn.tree.ExtraTreeClassifier
• the main subclasses of sklearn.ensemble.BaseForest for classification and embedding, i.e.:
  • sklearn.ensemble.RandomTreesEmbedding
  • sklearn.ensemble.RandomForestClassifier
  • sklearn.ensemble.ExtraTreesClassifier
• sklearn.linear_model.LogisticRegression
• sklearn.linear_model.LogisticRegressionCV
• sklearn.CalibratedClassifierCV after the merge of #17541

[mlant]

Thank you, I think it’s clear. I’ll begin tomorow, if I have some issues I’ll come back to you.

[rth]

I think we could probably directly add this as a common tests (and maybe skip estimators that fail at first)? It’s very similar in spirit to https://github.com/scikit-learn/scikit-learn/blob/46485a93beccd79d0e3563512e8385b3e5667524/sklearn/utils/estimator_checks.py#L972 so I imagine we could add a similar def check_class_weights_invariance(name, estimator_orig) and only run it here if the estimator has the “class_weight” init parameter.

Thanks for raising this @jjerphan !