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TST avoid FutureWarning due to n_features_in_ deprecation in Dummy* #20963

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Merged
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Sep 7, 2021
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TST avoid FutureWarning due to n_features_in_ deprecation in Dummy* #20963

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TST avoid FutureWarning due to n_features_in_ deprecation in Dummy*
glemaitre Sep 7, 2021
c436909
filterwarnings in test_dummy.py
glemaitre Sep 7, 2021
d5ab69b
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glemaitre Sep 7, 2021
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2 changes: 1 addition & 1 deletion sklearn/tests/test_calibration.py
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Expand Up @@ -613,7 +613,7 @@ def test_calibration_votingclassifier():
# defined via a property that only works when voting="soft".
X, y = make_classification(n_samples=10, n_features=5, n_classes=2, random_state=7)
vote = VotingClassifier(
estimators=[("dummy" + str(i), DummyClassifier()) for i in range(3)],
estimators=[("lr" + str(i), LogisticRegression()) for i in range(3)],
voting="soft",
)
vote.fit(X, y)
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2 changes: 2 additions & 0 deletions sklearn/tests/test_dummy.py
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Expand Up @@ -726,6 +726,8 @@ def test_dtype_of_classifier_probas(strategy):
assert probas.dtype == np.float64


# TODO: remove in 1.2
@pytest.mark.filterwarnings("ignore:`n_features_in_` is deprecated")
@pytest.mark.parametrize("Dummy", (DummyRegressor, DummyClassifier))
def test_n_features_in_(Dummy):
X = [[1, 2]]
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1 change: 1 addition & 0 deletions sklearn/tests/test_multioutput.py
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Expand Up @@ -615,6 +615,7 @@ def fit(self, X, y, sample_weight=None, **fit_params):
return super().fit(X, y, sample_weight)


@pytest.mark.filterwarnings("ignore:`n_features_in_` is deprecated")
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@glemaitre glemaitre Sep 7, 2021 •
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This one is worrying me. Here, it shows that a user will see the warning for some meta-estimators using the dummy estimator. Indeed, for the meta-estimators setting n_features_in_ depending on the inner estimator, we will check n_features_in_ and it will raise the warning with the dummy estimators.

It might be less of a big deal because it is only a dummy estimator and maybe not that many people are using it in a meta-estimator.

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@thomasjpfan thomasjpfan Sep 7, 2021 •
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If it gets annoying for users, we can define our own hasattr that ignores future warnings emitted by sklearn.

Edit: Most likely better to use a @property and @available_if. (If @available_if works with properties)

@pytest.mark.parametrize(
"estimator, dataset",
[
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