Cleanup the train function. (#7377)

* Move attribute setter to callback.
* Remove the internal train function.
* Remove unnecessary initialization.

python-package/xgboost/callback.py

@@ -11,7 +11,7 @@ from typing import Sequence
 import numpy
 
 from . import rabit
-from .core import Booster, DMatrix, XGBoostError
+from .core import Booster, DMatrix, XGBoostError, _get_booster_layer_trees
 from .compat import STRING_TYPES
 
 
@@ -149,6 +149,25 @@ class CallbackContainer:
                 assert isinstance(model.cvfolds, list), msg
             else:
                 assert isinstance(model, Booster), msg
+
+        if not self.is_cv:
+            num_parallel_tree, _ = _get_booster_layer_trees(model)
+            if model.attr('best_score') is not None:
+                model.best_score = float(cast(str, model.attr('best_score')))
+                model.best_iteration = int(cast(str, model.attr('best_iteration')))
+                # num_class is handled internally
+                model.set_attr(
+                    best_ntree_limit=str((model.best_iteration + 1) * num_parallel_tree)
+                )
+                model.best_ntree_limit = int(cast(str, model.attr("best_ntree_limit")))
+            else:
+                # Due to compatibility with version older than 1.4, these attributes are
+                # added to Python object even if early stopping is not used.
+                model.best_iteration = model.num_boosted_rounds() - 1
+                model.set_attr(best_iteration=str(model.best_iteration))
+                model.best_ntree_limit = (model.best_iteration + 1) * num_parallel_tree
+                model.set_attr(best_ntree_limit=str(model.best_ntree_limit))
+
         return model
 
     def before_iteration(

python-package/xgboost/training.py

@@ -5,10 +5,10 @@
 import copy
 import os
 import warnings
-from typing import Optional, Dict, Any, Union, Tuple, cast, Sequence
+from typing import Optional, Dict, Any, Union, Tuple, Sequence
 
 import numpy as np
-from .core import Booster, DMatrix, XGBoostError, _get_booster_layer_trees
+from .core import Booster, DMatrix, XGBoostError, _deprecate_positional_args
 from .core import Metric, Objective
 from .compat import (SKLEARN_INSTALLED, XGBStratifiedKFold)
 from . import callback
@@ -45,93 +45,12 @@ def _configure_custom_metric(
     return eval_metric
 
 
-def _train_internal(
+@_deprecate_positional_args
-    params: Dict[str, Any],
-    dtrain: DMatrix,
-    num_boost_round: int = 10,
-    evals: Optional[Sequence[Tuple[DMatrix, str]]] = None,
-    obj: Optional[Objective] = None,
-    feval: Optional[Metric] = None,
-    custom_metric: Optional[Metric] = None,
-    xgb_model: Optional[Union[str, os.PathLike, Booster, bytearray]] = None,
-    callbacks: Optional[Sequence[callback.TrainingCallback]] = None,
-    evals_result: callback.TrainingCallback.EvalsLog = None,
-    maximize: Optional[bool] = None,
-    verbose_eval: Optional[Union[bool, int]] = True,
-    early_stopping_rounds: Optional[int] = None,
-) -> Booster:
-    """internal training function"""
-    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])
-
-    if xgb_model is not None:
-        bst = Booster(params, [dtrain] + [d[0] for d in evals], model_file=xgb_model)
-
-    start_iteration = 0
-
-    _assert_new_callback(callbacks)
-    if verbose_eval:
-        verbose_eval = 1 if verbose_eval is True else verbose_eval
-        callbacks.append(callback.EvaluationMonitor(period=verbose_eval))
-    if early_stopping_rounds:
-        callbacks.append(
-            callback.EarlyStopping(rounds=early_stopping_rounds, maximize=maximize)
-        )
-    cb_container = callback.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,
-    )
-
-    bst = cb_container.before_training(bst)
-
-    for i in range(start_iteration, num_boost_round):
-        if cb_container.before_iteration(bst, i, dtrain, evals):
-            break
-        bst.update(dtrain, i, obj)
-        if cb_container.after_iteration(bst, i, dtrain, evals):
-            break
-
-    bst = cb_container.after_training(bst)
-
-    if evals_result is not None:
-        evals_result.update(cb_container.history)
-
-    # These should be moved into callback functions `after_training`, but until old
-    # callbacks are removed, the train function is the only place for setting the
-    # attributes.
-    num_parallel_tree, _ = _get_booster_layer_trees(bst)
-    if bst.attr('best_score') is not None:
-        bst.best_score = float(cast(str, bst.attr('best_score')))
-        bst.best_iteration = int(cast(str, bst.attr('best_iteration')))
-        # num_class is handled internally
-        bst.set_attr(
-            best_ntree_limit=str((bst.best_iteration + 1) * num_parallel_tree)
-        )
-        bst.best_ntree_limit = int(cast(str, bst.attr("best_ntree_limit")))
-    else:
-        # Due to compatibility with version older than 1.4, these attributes are added
-        # to Python object even if early stopping is not used.
-        bst.best_iteration = bst.num_boosted_rounds() - 1
-        bst.set_attr(best_iteration=str(bst.best_iteration))
-        bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
-        bst.set_attr(best_ntree_limit=str(bst.best_ntree_limit))
-
-    # Copy to serialise and unserialise booster to reset state and free
-    # training memory
-    return bst.copy()
-
-
 def train(
     params: Dict[str, Any],
     dtrain: DMatrix,
     num_boost_round: int = 10,
+    *,
     evals: Optional[Sequence[Tuple[DMatrix, str]]] = None,
     obj: Optional[Objective] = None,
     feval: Optional[Metric] = None,
@@ -223,22 +142,48 @@ def train(
     -------
     Booster : a trained booster model
     """
-    bst = _train_internal(
+
-        params,
+    callbacks = [] if callbacks is None else copy.copy(list(callbacks))
-        dtrain,
+    metric_fn = _configure_custom_metric(feval, custom_metric)
-        num_boost_round=num_boost_round,
+    evals = list(evals) if evals else []
-        evals=evals,
+
-        obj=obj,
+    bst = Booster(params, [dtrain] + [d[0] for d in evals], model_file=xgb_model)
-        feval=feval,
+    start_iteration = 0
-        xgb_model=xgb_model,
+
-        callbacks=callbacks,
+    _assert_new_callback(callbacks)
-        verbose_eval=verbose_eval,
+    if verbose_eval:
-        evals_result=evals_result,
+        verbose_eval = 1 if verbose_eval is True else verbose_eval
-        maximize=maximize,
+        callbacks.append(callback.EvaluationMonitor(period=verbose_eval))
-        early_stopping_rounds=early_stopping_rounds,
+    if early_stopping_rounds:
-        custom_metric=custom_metric,
+        callbacks.append(
+            callback.EarlyStopping(rounds=early_stopping_rounds, maximize=maximize)
         )
-    return bst
+    cb_container = callback.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,
+    )
+
+    bst = cb_container.before_training(bst)
+
+    for i in range(start_iteration, num_boost_round):
+        if cb_container.before_iteration(bst, i, dtrain, evals):
+            break
+        bst.update(dtrain, i, obj)
+        if cb_container.after_iteration(bst, i, dtrain, evals):
+            break
+
+    bst = cb_container.after_training(bst)
+
+    if evals_result is not None:
+        evals_result.update(cb_container.history)
+
+    # Copy to serialise and unserialise booster to reset state and free
+    # training memory
+    return bst.copy()
 
 
 class CVPack: