Support more sklearn tags for testing. (#10230)

python-package/xgboost/sklearn.py

@@ -782,7 +782,10 @@ class XGBModel(XGBModelBase):
 
     def _more_tags(self) -> Dict[str, bool]:
         """Tags used for scikit-learn data validation."""
-        return {"allow_nan": True, "no_validation": True}
+        tags = {"allow_nan": True, "no_validation": True}
+        if hasattr(self, "kwargs") and self.kwargs.get("updater") == "shotgun":
+            tags["non_deterministic"] = True
+        return tags
 
     def __sklearn_is_fitted__(self) -> bool:
         return hasattr(self, "_Booster")
@@ -1439,6 +1442,11 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
     ) -> None:
         super().__init__(objective=objective, **kwargs)
 
+    def _more_tags(self) -> Dict[str, bool]:
+        tags = super()._more_tags()
+        tags["multilabel"] = True
+        return tags
+
     @_deprecate_positional_args
     def fit(
         self,
@@ -1717,6 +1725,12 @@ class XGBRegressor(XGBModel, XGBRegressorBase):
     ) -> None:
         super().__init__(objective=objective, **kwargs)
 
+    def _more_tags(self) -> Dict[str, bool]:
+        tags = super()._more_tags()
+        tags["multioutput"] = True
+        tags["multioutput_only"] = False
+        return tags
+
 
 @xgboost_model_doc(
     "scikit-learn API for XGBoost random forest regression.",

tests/python/test_with_sklearn.py

@@ -1300,19 +1300,11 @@ def test_estimator_reg(estimator, check):
         ):
             estimator.fit(X, y)
         return
-    if (
+    elif os.environ["PYTEST_CURRENT_TEST"].find("check_regressor_multioutput") != -1:
-        os.environ["PYTEST_CURRENT_TEST"].find("check_estimators_overwrite_params")
+        # sklearn requires float64
-        != -1
+        with pytest.raises(AssertionError, match="Got float32"):
-    ):
+            check(estimator)
-        # A hack to pass the scikit-learn parameter mutation tests.  XGBoost regressor
+    else:
-        # returns actual internal default values for parameters in `get_params`, but
-        # those are set as `None` in sklearn interface to avoid duplication.  So we fit
-        # a dummy model and obtain the default parameters here for the mutation tests.
-        from sklearn.datasets import make_regression
-
-        X, y = make_regression(n_samples=2, n_features=1)
-        estimator.set_params(**xgb.XGBRegressor().fit(X, y).get_params())
-
         check(estimator)
 
 
@@ -1475,3 +1467,19 @@ def test_fit_none() -> None:
 
     with pytest.raises(ValueError, match="labels"):
         xgb.XGBRegressor().fit(X, None)
+
+
+def test_tags() -> None:
+    for reg in [xgb.XGBRegressor(), xgb.XGBRFRegressor()]:
+        tags = reg._more_tags()
+        assert "non_deterministic" not in tags
+        assert tags["multioutput"] is True
+        assert tags["multioutput_only"] is False
+
+    for clf in [xgb.XGBClassifier()]:
+        tags = clf._more_tags()
+        assert "multioutput" not in tags
+        assert tags["multilabel"] is True
+
+    tags = xgb.XGBRanker()._more_tags()
+    assert "multioutput" not in tags