Add sample_weight to eval_metric

python-package/xgboost/sklearn.py

@@ -123,6 +123,24 @@ def inner(y_score: np.ndarray, dmatrix: DMatrix) -> Tuple[str, float]:
 
     return inner
 
+def _sample_weight_metric_decorator(func: Callable) -> Metric:
+    """Decorate a metric function from sklearn.
+
+    Converts an metric function that uses the typical sklearn metric signature so that it
+    is compatible with :py:func:`train`
+
+    """
+
+    def inner(y_score: np.ndarray, dmatrix: DMatrix) -> Tuple[str, float]:
+        y_true = dmatrix.get_label()
+        sample_weight = dmatrix.get_weight()
+        try:
+            return func.__name__, func(y_true, y_score, sample_weight=sample_weight)
+        except TypeError:
+            #func has no sample_weight
+            return func.__name__, func(y_true, y_score)
+
+    return inner
 
 __estimator_doc = """
     n_estimators : int
@@ -819,6 +837,7 @@ def _configure_fit(
         params: Dict[str, Any],
         early_stopping_rounds: Optional[int],
         callbacks: Optional[Sequence[TrainingCallback]],
+        sample_weight_metric: Optional[bool],
     ) -> Tuple[
         Optional[Union[Booster, str, "XGBModel"]],
         Optional[Metric],
@@ -865,7 +884,10 @@ def _duplicated(parameter: str) -> None:
                 metric = eval_metric
             elif callable(eval_metric):
                 # Parameter from constructor or set_params
-                metric = _metric_decorator(eval_metric)
+                if sample_weight_metric:
+                    metric = _sample_weight_metric_decorator(eval_metric)
+                else:
+                    metric = _metric_decorator(eval_metric)
             else:
                 params.update({"eval_metric": eval_metric})
 
@@ -1029,7 +1051,7 @@ def fit(
                 early_stopping_rounds,
                 callbacks,
             ) = self._configure_fit(
-                xgb_model, eval_metric, params, early_stopping_rounds, callbacks
+                xgb_model, eval_metric, params, early_stopping_rounds, callbacks, sample_weight != None
             )
             self._Booster = train(
                 params,
@@ -1475,7 +1497,7 @@ def fit(
                 early_stopping_rounds,
                 callbacks,
             ) = self._configure_fit(
-                xgb_model, eval_metric, params, early_stopping_rounds, callbacks
+                xgb_model, eval_metric, params, early_stopping_rounds, callbacks, sample_weight != None
             )
             train_dmatrix, evals = _wrap_evaluation_matrices(
                 missing=self.missing,
@@ -1974,7 +1996,7 @@ def fit(
                 early_stopping_rounds,
                 callbacks,
             ) = self._configure_fit(
-                xgb_model, eval_metric, params, early_stopping_rounds, callbacks
+                xgb_model, eval_metric, params, early_stopping_rounds, callbacks, sample_weight != None
             )
             if callable(metric):
                 raise ValueError(