Move feature weight to skl parameters. (#9506)

python-package/xgboost/dask/__init__.py

@@ -1639,6 +1639,10 @@ async def _fit_async(
         feature_weights: Optional[_DaskCollection],
     ) -> _DaskCollection:
         params = self.get_xgb_params()
+        model, metric, params, feature_weights = self._configure_fit(
+            xgb_model, params, feature_weights
+        )
+
         dtrain, evals = await _async_wrap_evaluation_matrices(
             client=self.client,
             device=self.device,
@@ -1665,7 +1669,6 @@ async def _fit_async(
             obj: Optional[Callable] = _objective_decorator(self.objective)
         else:
             obj = None
-        model, metric, params = self._configure_fit(xgb_model, params)
         results = await self.client.sync(
             _train_async,
             asynchronous=True,
@@ -1729,6 +1732,10 @@ async def _fit_async(
         feature_weights: Optional[_DaskCollection],
     ) -> "DaskXGBClassifier":
         params = self.get_xgb_params()
+        model, metric, params, feature_weights = self._configure_fit(
+            xgb_model, params, feature_weights
+        )
+
         dtrain, evals = await _async_wrap_evaluation_matrices(
             self.client,
             device=self.device,
@@ -1773,7 +1780,6 @@ async def _fit_async(
             obj: Optional[Callable] = _objective_decorator(self.objective)
         else:
             obj = None
-        model, metric, params = self._configure_fit(xgb_model, params)
         results = await self.client.sync(
             _train_async,
             asynchronous=True,
@@ -1953,6 +1959,9 @@ async def _fit_async(
         feature_weights: Optional[_DaskCollection],
     ) -> "DaskXGBRanker":
         params = self.get_xgb_params()
+        model, metric, params, feature_weights = self._configure_fit(
+            xgb_model, params, feature_weights
+        )
         dtrain, evals = await _async_wrap_evaluation_matrices(
             self.client,
             device=self.device,
@@ -1974,7 +1983,6 @@ async def _fit_async(
             enable_categorical=self.enable_categorical,
             feature_types=self.feature_types,
         )
-        model, metric, params = self._configure_fit(xgb_model, params)
         results = await self.client.sync(
             _train_async,
             asynchronous=True,

python-package/xgboost/sklearn.py

@@ -389,7 +389,13 @@ def task(i: int) -> float:
         Used for specifying feature types without constructing a dataframe. See
         :py:class:`DMatrix` for details.
 
-    max_cat_to_onehot : {Optional[int]}
+    feature_weights : Optional[ArrayLike]
+
+        Weight for each feature, defines the probability of each feature being selected
+        when colsample is being used.  All values must be greater than 0, otherwise a
+        `ValueError` is thrown.
+
+    max_cat_to_onehot : Optional[int]
 
         .. versionadded:: 1.6.0
 
@@ -607,7 +613,7 @@ def _wrap_evaluation_matrices(
     qid: Optional[Any],
     sample_weight: Optional[Any],
     base_margin: Optional[Any],
-    feature_weights: Optional[Any],
+    feature_weights: Optional[ArrayLike],
     eval_set: Optional[Sequence[Tuple[Any, Any]]],
     sample_weight_eval_set: Optional[Sequence[Any]],
     base_margin_eval_set: Optional[Sequence[Any]],
@@ -753,6 +759,7 @@ def __init__(
         validate_parameters: Optional[bool] = None,
         enable_categorical: bool = False,
         feature_types: Optional[FeatureTypes] = None,
+        feature_weights: Optional[ArrayLike] = None,
         max_cat_to_onehot: Optional[int] = None,
         max_cat_threshold: Optional[int] = None,
         multi_strategy: Optional[str] = None,
@@ -799,6 +806,7 @@ def __init__(
         self.validate_parameters = validate_parameters
         self.enable_categorical = enable_categorical
         self.feature_types = feature_types
+        self.feature_weights = feature_weights
         self.max_cat_to_onehot = max_cat_to_onehot
         self.max_cat_threshold = max_cat_threshold
         self.multi_strategy = multi_strategy
@@ -895,6 +903,7 @@ def _wrapper_params(self) -> Set[str]:
             "early_stopping_rounds",
             "callbacks",
             "feature_types",
+            "feature_weights",
         }
         return wrapper_specific
 
@@ -1065,10 +1074,12 @@ def _configure_fit(
         self,
         booster: Optional[Union[Booster, "XGBModel", str]],
         params: Dict[str, Any],
+        feature_weights: Optional[ArrayLike],
     ) -> Tuple[
         Optional[Union[Booster, str, "XGBModel"]],
         Optional[Metric],
         Dict[str, Any],
+        Optional[ArrayLike],
     ]:
         """Configure parameters for :py:meth:`fit`."""
         if isinstance(booster, XGBModel):
@@ -1101,13 +1112,23 @@ def _duplicated(parameter: str) -> None:
             else:
                 params.update({"eval_metric": self.eval_metric})
 
+        if feature_weights is not None:
+            _deprecated("feature_weights")
+        if feature_weights is not None and self.feature_weights is not None:
+            _duplicated("feature_weights")
+        feature_weights = (
+            self.feature_weights
+            if self.feature_weights is not None
+            else feature_weights
+        )
+
         tree_method = params.get("tree_method", None)
         if self.enable_categorical and tree_method == "exact":
             raise ValueError(
                 "Experimental support for categorical data is not implemented for"
                 " current tree method yet."
             )
-        return model, metric, params
+        return model, metric, params, feature_weights
 
     def _create_dmatrix(self, ref: Optional[DMatrix], **kwargs: Any) -> DMatrix:
         # Use `QuantileDMatrix` to save memory.
@@ -1184,12 +1205,19 @@ def fit(
             A list of the form [M_1, M_2, ..., M_n], where each M_i is an array like
             object storing base margin for the i-th validation set.
         feature_weights :
-            Weight for each feature, defines the probability of each feature being
-            selected when colsample is being used.  All values must be greater than 0,
-            otherwise a `ValueError` is thrown.
+
+            .. deprecated:: 3.0.0
+
+            Use `feature_weights` in :py:meth:`__init__` or :py:meth:`set_params`
+            instead.
 
         """
         with config_context(verbosity=self.verbosity):
+            params = self.get_xgb_params()
+            model, metric, params, feature_weights = self._configure_fit(
+                xgb_model, params, feature_weights
+            )
+
             evals_result: TrainingCallback.EvalsLog = {}
             train_dmatrix, evals = _wrap_evaluation_matrices(
                 missing=self.missing,
@@ -1209,15 +1237,13 @@ def fit(
                 enable_categorical=self.enable_categorical,
                 feature_types=self.feature_types,
             )
-            params = self.get_xgb_params()
 
             if callable(self.objective):
                 obj: Optional[Objective] = _objective_decorator(self.objective)
                 params["objective"] = "reg:squarederror"
             else:
                 obj = None
 
-            model, metric, params = self._configure_fit(xgb_model, params)
             self._Booster = train(
                 params,
                 train_dmatrix,
@@ -1631,7 +1657,9 @@ def fit(
                     params["objective"] = "multi:softprob"
                 params["num_class"] = self.n_classes_
 
-            model, metric, params = self._configure_fit(xgb_model, params)
+            model, metric, params, feature_weights = self._configure_fit(
+                xgb_model, params, feature_weights
+            )
             train_dmatrix, evals = _wrap_evaluation_matrices(
                 missing=self.missing,
                 X=X,
@@ -2148,8 +2176,9 @@ def fit(
             evals_result: TrainingCallback.EvalsLog = {}
             params = self.get_xgb_params()
 
-            model, metric, params = self._configure_fit(xgb_model, params)
-
+            model, metric, params, feature_weights = self._configure_fit(
+                xgb_model, params, feature_weights
+            )
             self._Booster = train(
                 params,
                 train_dmatrix,

python-package/xgboost/spark/core.py

@@ -641,6 +641,7 @@ def __init__(self) -> None:
             repartition_random_shuffle=False,
             feature_names=None,
             feature_types=None,
+            feature_weights=None,
             arbitrary_params_dict={},
             launch_tracker_on_driver=True,
         )

python-package/xgboost/spark/data.py

@@ -352,6 +352,7 @@ def pred_contribs(
         missing=model.missing,
         nthread=model.n_jobs,
         feature_types=model.feature_types,
+        feature_weights=model.feature_weights,
         enable_categorical=model.enable_categorical,
     )
     return model.get_booster().predict(

python-package/xgboost/testing/shared.py

@@ -63,9 +63,13 @@ def get_feature_weights(
     """Get feature weights using the demo parser."""
     with tempfile.TemporaryDirectory() as tmpdir:
         colsample_bynode = 0.5
-        reg = model(tree_method=tree_method, colsample_bynode=colsample_bynode)
+        reg = model(
+            tree_method=tree_method,
+            colsample_bynode=colsample_bynode,
+            feature_weights=fw,
+        )
 
-        reg.fit(X, y, feature_weights=fw)
+        reg.fit(X, y)
         model_path = os.path.join(tmpdir, "model.json")
         reg.save_model(model_path)
         with open(model_path, "r", encoding="utf-8") as fd:

tests/python/test_with_sklearn.py

@@ -1212,6 +1212,10 @@ def test_feature_weights(tree_method):
     assert poly_increasing[0] > 0.08
     assert poly_decreasing[0] < -0.08
 
+    reg = xgb.XGBRegressor(feature_weights=np.ones((kCols, )))
+    with pytest.raises(ValueError, match="Use the one in"):
+        reg.fit(X, y, feature_weights=np.ones((kCols, )))
+
 
 def run_boost_from_prediction_binary(tree_method, X, y, as_frame: Optional[Callable]):
     """