Remove deprecated feval.

demo/guide-python/cross_validation.py

@@ -2,6 +2,7 @@
 Demo for using cross validation
 ===============================
 """
+
 import os
 
 import numpy as np
@@ -83,9 +84,12 @@ def logregobj(preds, dtrain):
 
 def evalerror(preds, dtrain):
     labels = dtrain.get_label()
+    preds = 1.0 / (1.0 + np.exp(-preds))
     return "error", float(sum(labels != (preds > 0.0))) / len(labels)
 
 
 param = {"max_depth": 2, "eta": 1}
 # train with customized objective
-xgb.cv(param, dtrain, num_round, nfold=5, seed=0, obj=logregobj, feval=evalerror)
+xgb.cv(
+    param, dtrain, num_round, nfold=5, seed=0, obj=logregobj, custom_metric=evalerror
+)

doc/python/python_api.rst

@@ -37,6 +37,7 @@ Core Data Structure
 .. autoclass:: xgboost.Booster
     :members:
     :show-inheritance:
+    :special-members: __getitem__
 
 .. autoclass:: xgboost.DataIter
     :members:

python-package/xgboost/dask/__init__.py

@@ -766,7 +766,6 @@ async def _train_async(
     num_boost_round: int,
     evals: Optional[Sequence[Tuple[DaskDMatrix, str]]],
     obj: Optional[Objective],
-    feval: Optional[Metric],
     early_stopping_rounds: Optional[int],
     verbose_eval: Union[int, bool],
     xgb_model: Optional[Booster],
@@ -816,7 +815,6 @@ def do_train(  # pylint: disable=too-many-positional-arguments
                 evals_result=local_history,
                 evals=evals if len(evals) != 0 else None,
                 obj=obj,
-                feval=feval,
                 custom_metric=custom_metric,
                 early_stopping_rounds=early_stopping_rounds,
                 verbose_eval=verbose_eval,
@@ -870,7 +868,6 @@ def train(  # pylint: disable=unused-argument
     *,
     evals: Optional[Sequence[Tuple[DaskDMatrix, str]]] = None,
     obj: Optional[Objective] = None,
-    feval: Optional[Metric] = None,
     early_stopping_rounds: Optional[int] = None,
     xgb_model: Optional[Booster] = None,
     verbose_eval: Union[int, bool] = True,
@@ -1675,7 +1672,6 @@ async def _fit_async(
             num_boost_round=self.get_num_boosting_rounds(),
             evals=evals,
             obj=obj,
-            feval=None,
             custom_metric=metric,
             verbose_eval=verbose,
             early_stopping_rounds=self.early_stopping_rounds,
@@ -1784,7 +1780,6 @@ async def _fit_async(
             num_boost_round=self.get_num_boosting_rounds(),
             evals=evals,
             obj=obj,
-            feval=None,
             custom_metric=metric,
             verbose_eval=verbose,
             early_stopping_rounds=self.early_stopping_rounds,
@@ -1986,7 +1981,6 @@ async def _fit_async(
             num_boost_round=self.get_num_boosting_rounds(),
             evals=evals,
             obj=None,
-            feval=None,
             custom_metric=metric,
             verbose_eval=verbose,
             early_stopping_rounds=self.early_stopping_rounds,

python-package/xgboost/sklearn.py

@@ -426,7 +426,7 @@ def task(i: int) -> float:
 
         Metric used for monitoring the training result and early stopping.  It can be a
         string or list of strings as names of predefined metric in XGBoost (See
-        doc/parameter.rst), one of the metrics in :py:mod:`sklearn.metrics`, or any
+        :doc:`/parameter`), one of the metrics in :py:mod:`sklearn.metrics`, or any
         other user defined metric that looks like `sklearn.metrics`.
 
         If custom objective is also provided, then custom metric should implement the

python-package/xgboost/testing/__init__.py

@@ -662,9 +662,29 @@ def predictor_equal(lhs: xgb.DMatrix, rhs: xgb.DMatrix) -> bool:
 M = TypeVar("M", xgb.Booster, xgb.XGBModel)
 
 
-def eval_error_metric(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, np.float64]:
-    """Evaluation metric for xgb.train"""
+def logregobj(preds: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[np.ndarray, np.ndarray]:
+    """Binary regression custom objective."""
+    labels = dtrain.get_label()
+    preds = 1.0 / (1.0 + np.exp(-preds))
+    grad = preds - labels
+    hess = preds * (1.0 - preds)
+    return grad, hess
+
+
+def eval_error_metric(
+    predt: np.ndarray, dtrain: xgb.DMatrix, rev_link: bool
+) -> Tuple[str, np.float64]:
+    """Evaluation metric for xgb.train.
+
+    Parameters
+    ----------
+    rev_link : Whether the metric needs to apply the reverse link function (activation).
+
+    """
     label = dtrain.get_label()
+    if rev_link:
+        predt = 1.0 / (1.0 + np.exp(-predt))
+    assert (0.0 <= predt).all() and (predt <= 1.0).all()
     r = np.zeros(predt.shape)
     gt = predt > 0.5
     if predt.size == 0:
@@ -675,8 +695,15 @@ def eval_error_metric(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, np.f
     return "CustomErr", np.sum(r)
 
 
-def eval_error_metric_skl(y_true: np.ndarray, y_score: np.ndarray) -> np.float64:
+def eval_error_metric_skl(
+    y_true: np.ndarray, y_score: np.ndarray, rev_link: bool = False
+) -> np.float64:
     """Evaluation metric that looks like metrics provided by sklearn."""
+
+    if rev_link:
+        y_score = 1.0 / (1.0 + np.exp(-y_score))
+    assert (0.0 <= y_score).all() and (y_score <= 1.0).all()
+
     r = np.zeros(y_score.shape)
     gt = y_score > 0.5
     r[gt] = 1 - y_true[gt]

python-package/xgboost/training.py

@@ -3,7 +3,6 @@
 """Training Library containing training routines."""
 import copy
 import os
-import warnings
 from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union, cast
 
 import numpy as np
@@ -28,26 +27,6 @@
 _CVFolds = Sequence["CVPack"]
 
 
-def _configure_custom_metric(
-    feval: Optional[Metric], custom_metric: Optional[Metric]
-) -> Optional[Metric]:
-    if feval is not None:
-        link = (
-            "https://xgboost.readthedocs.io/en/latest/tutorials/custom_metric_obj.html"
-        )
-        warnings.warn(
-            "`feval` is deprecated, use `custom_metric` instead.  They have "
-            "different behavior when custom objective is also used."
-            f"See {link} for details on the `custom_metric`."
-        )
-    if feval is not None and custom_metric is not None:
-        raise ValueError(
-            "Both `feval` and `custom_metric` are supplied.  Use `custom_metric` instead."
-        )
-    eval_metric = custom_metric if custom_metric is not None else feval
-    return eval_metric
-
-
 @_deprecate_positional_args
 def train(
     params: Dict[str, Any],
@@ -56,7 +35,6 @@ def train(
     *,
     evals: Optional[Sequence[Tuple[DMatrix, str]]] = None,
     obj: Optional[Objective] = None,
-    feval: Optional[Metric] = None,
     maximize: Optional[bool] = None,
     early_stopping_rounds: Optional[int] = None,
     evals_result: Optional[TrainingCallback.EvalsLog] = None,
@@ -81,23 +59,27 @@ def train(
     obj
         Custom objective function.  See :doc:`Custom Objective
         </tutorials/custom_metric_obj>` for details.
-    feval :
-        .. deprecated:: 1.6.0
-            Use `custom_metric` instead.
     maximize :
-        Whether to maximize feval.
+        Whether to maximize custom_metric.
+
     early_stopping_rounds :
+
         Activates early stopping. Validation metric needs to improve at least once in
         every **early_stopping_rounds** round(s) to continue training.
+
         Requires at least one item in **evals**.
+
         The method returns the model from the last iteration (not the best one).  Use
-        custom callback or model slicing if the best model is desired.
-        If there's more than one item in **evals**, the last entry will be used for early
-        stopping.
+        custom callback :py:class:`~xgboost.callback.EarlyStopping` or :py:meth:`model
+        slicing <xgboost.Booster.__getitem__>` if the best model is desired.  If there's
+        more than one item in **evals**, the last entry will be used for early stopping.
+
         If there's more than one metric in the **eval_metric** parameter given in
         **params**, the last metric will be used for early stopping.
+
         If early stopping occurs, the model will have two additional fields:
         ``bst.best_score``, ``bst.best_iteration``.
+
     evals_result :
         This dictionary stores the evaluation results of all the items in watchlist.
 
@@ -113,15 +95,22 @@ def train(
 
     verbose_eval :
         Requires at least one item in **evals**.
+
         If **verbose_eval** is True then the evaluation metric on the validation set is
         printed at each boosting stage.
-        If **verbose_eval** is an integer then the evaluation metric on the validation set
-        is printed at every given **verbose_eval** boosting stage. The last boosting stage
-        / the boosting stage found by using **early_stopping_rounds** is also printed.
-        Example: with ``verbose_eval=4`` and at least one item in **evals**, an evaluation metric
-        is printed every 4 boosting stages, instead of every boosting stage.
+
+        If **verbose_eval** is an integer then the evaluation metric on the validation
+        set is printed at every given **verbose_eval** boosting stage. The last boosting
+        stage / the boosting stage found by using **early_stopping_rounds** is also
+        printed.
+
+        Example: with ``verbose_eval=4`` and at least one item in **evals**, an
+        evaluation metric is printed every 4 boosting stages, instead of every boosting
+        stage.
+
     xgb_model :
         Xgb model to be loaded before training (allows training continuation).
+
     callbacks :
         List of callback functions that are applied at end of each iteration.
         It is possible to use predefined callbacks by using
@@ -145,15 +134,17 @@ def train(
         .. versionadded 1.6.0
 
         Custom metric function.  See :doc:`Custom Metric </tutorials/custom_metric_obj>`
-        for details.
+        for details. The metric receives transformed predictioin (after applying the
+        reverse link function) when using a builtin objective, and raw output when using
+        a custom objective.
 
     Returns
     -------
     Booster : a trained booster model
+
     """
 
     callbacks = [] if callbacks is None else copy.copy(list(callbacks))
-    metric_fn = _configure_custom_metric(feval, custom_metric)
     evals = list(evals) if evals else []
 
     bst = Booster(params, [dtrain] + [d[0] for d in evals], model_file=xgb_model)
@@ -165,12 +156,7 @@ def train(
     if early_stopping_rounds:
         callbacks.append(EarlyStopping(rounds=early_stopping_rounds, maximize=maximize))
     cb_container = CallbackContainer(
-        callbacks,
-        metric=metric_fn,
-        # For old `feval` parameter, the behavior is unchanged.  For the new
-        # `custom_metric`, it will receive proper prediction result when custom objective
-        # is not used.
-        output_margin=callable(obj) or metric_fn is feval,
+        callbacks, metric=custom_metric, output_margin=callable(obj)
     )
 
     bst = cb_container.before_training(bst)
@@ -423,7 +409,6 @@ def cv(
     folds: XGBStratifiedKFold = None,
     metrics: Sequence[str] = (),
     obj: Optional[Objective] = None,
-    feval: Optional[Metric] = None,
     maximize: Optional[bool] = None,
     early_stopping_rounds: Optional[int] = None,
     fpreproc: Optional[FPreProcCallable] = None,
@@ -464,11 +449,9 @@ def cv(
         Custom objective function.  See :doc:`Custom Objective
         </tutorials/custom_metric_obj>` for details.
 
-    feval : function
-        .. deprecated:: 1.6.0
-            Use `custom_metric` instead.
     maximize : bool
-        Whether to maximize feval.
+        Whether to maximize the evaluataion metric (score or error).
+
     early_stopping_rounds: int
         Activates early stopping. Cross-Validation metric (average of validation
         metric computed over CV folds) needs to improve at least once in
@@ -559,8 +542,6 @@ def cv(
         shuffle=shuffle,
     )
 
-    metric_fn = _configure_custom_metric(feval, custom_metric)
-
     # setup callbacks
     callbacks = [] if callbacks is None else copy.copy(list(callbacks))
 
@@ -570,10 +551,7 @@ def cv(
     if early_stopping_rounds:
         callbacks.append(EarlyStopping(rounds=early_stopping_rounds, maximize=maximize))
     callbacks_container = CallbackContainer(
-        callbacks,
-        metric=metric_fn,
-        is_cv=True,
-        output_margin=callable(obj) or metric_fn is feval,
+        callbacks, metric=custom_metric, is_cv=True, output_margin=callable(obj)
     )
 
     booster = _PackedBooster(cvfolds)

tests/ci_build/lint_python.py

@@ -27,6 +27,7 @@ class LintersPaths:
         "tests/python/test_dt.py",
         "tests/python/test_demos.py",
         "tests/python/test_eval_metrics.py",
+        "tests/python/test_early_stopping.py",
         "tests/python/test_multi_target.py",
         "tests/python/test_objectives.py",
         "tests/python/test_predict.py",
@@ -54,6 +55,7 @@ class LintersPaths:
         "demo/guide-python/callbacks.py",
         "demo/guide-python/categorical.py",
         "demo/guide-python/cat_pipeline.py",
+        "demo/guide-python/cross_validation.py",
         "demo/guide-python/feature_weights.py",
         "demo/guide-python/model_parser.py",
         "demo/guide-python/sklearn_parallel.py",