> pytest tests/python -k test_select_feature
============================================================= test session starts =============================================================
platform darwin -- Python 3.8.12, pytest-7.1.0, pluggy-1.0.0
Matplotlib: 3.4.3
Freetype: 2.10.4
rootdir: /Users/glemaitre/Documents/packages/xgboost/tests, configfile: pytest.ini
plugins: mpl-0.14.0, hypothesis-6.23.2, xdist-2.4.0, asdf-2.11.0, anyio-3.3.3, forked-1.3.0, cov-3.0.0
collected 334 items / 333 deselected / 1 selected                                                                                             

tests/python/test_with_sklearn.py F                                                                                                     [100%]

================================================================== FAILURES ===================================================================
_____________________________________________________________ test_select_feature _____________________________________________________________

    def test_select_feature():
        from sklearn.datasets import load_digits
        from sklearn.feature_selection import SelectFromModel
        digits = load_digits(n_class=2)
        y = digits['target']
        X = digits['data']
        cls = xgb.XGBClassifier()
        cls.fit(X, y)
        selector = SelectFromModel(cls, prefit=True, max_features=1)
>       X_selected = selector.transform(X)

tests/python/test_with_sklearn.py:326: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../scikit-learn/sklearn/feature_selection/_base.py:90: in transform
    return self._transform(X)
../scikit-learn/sklearn/feature_selection/_base.py:94: in _transform
    mask = self.get_support()
../scikit-learn/sklearn/feature_selection/_base.py:53: in get_support
    mask = self._get_support_mask()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = SelectFromModel(estimator=XGBClassifier(base_score=0.5, booster='gbtree',
                                        call...                                        random_state=0, reg_alpha=0, ...),
                max_features=1, prefit=True)

    def _get_support_mask(self):
        # SelectFromModel can directly call on transform.
        if self.prefit:
            estimator = self.estimator
        elif hasattr(self, "estimator_"):
            estimator = self.estimator_
        else:
            raise ValueError(
                "Either fit the model before transform or set"
                ' "prefit=True" while passing the fitted'
                " estimator to the constructor."
            )
        scores = _get_feature_importances(
            estimator=estimator,
            getter=self.importance_getter,
            transform_func="norm",
            norm_order=self.norm_order,
        )
        threshold = _calculate_threshold(estimator, scores, self.threshold)
        if self.max_features is not None:
            mask = np.zeros_like(scores, dtype=bool)
            candidate_indices = np.argsort(-scores, kind="mergesort")[
>               : self.max_features_
            ]
E           AttributeError: 'SelectFromModel' object has no attribute 'max_features_'

../scikit-learn/sklearn/feature_selection/_from_model.py:261: AttributeError