Reviewers' comments.

demo/guide-python/callbacks.py

@@ -97,7 +97,7 @@ def check(as_pickle):
     # Check point to a temporary directory for demo
     with tempfile.TemporaryDirectory() as tmpdir:
         # Use callback class from xgboost.callback
-        # Feel free to subclass/customize it to suite your need.
+        # Feel free to subclass/customize it to suit your need.
         check_point = xgb.callback.TrainingCheckPoint(directory=tmpdir,
                                                       rounds=rounds,
                                                       name='model')

doc/python/callbacks.rst

@@ -4,7 +4,7 @@ Callback Functions
 
 This document gives a basic walkthrough of callback function used in XGBoost Python
 package.  In XGBoost 1.3, a new callback interface is designed for Python package, which
-provides the flexiablity of designing various extension for training.  Also, XGBoost has a
+provides the flexiblity of designing various extension for training.  Also, XGBoost has a
 number of pre-defined callbacks for supporting early stopping, checkpoints etc.
 
 #######################
@@ -30,11 +30,11 @@ this callback function directly into XGBoost:
         r[gt] = 1 - label[gt]
         le = predt <= 0.5
         r[le] = label[le]
-        return 'PyError', np.sum(r)
+        return 'CustomErr', np.sum(r)
 
     # Specify which dataset and which metric should be used for early stopping.
     early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
-                                            metric_name='PyError',
+                                            metric_name='CustomErr',
                                             data_name='Train')
 
     booster = xgb.train(
@@ -48,7 +48,7 @@ this callback function directly into XGBoost:
         verbose_eval=False)
 
     dump = booster.get_dump(dump_format='json')
-    assert len(early_stop.stopping_history['Valid']['PyError']) == len(dump)
+    assert len(early_stop.stopping_history['Valid']['CustomErr']) == len(dump)
 
 ##########################
 Defining your own callback

python-package/xgboost/training.py

@@ -12,6 +12,8 @@
 def _configure_deprecated_callbacks(
         verbose_eval, early_stopping_rounds, maximize, start_iteration,
         num_boost_round, feval, evals_result, callbacks, show_stdv, cvfolds):
+    link = 'https://xgboost.readthedocs.io/en/latest/python/callbacks.html'
+    raise DeprecationWarning(f'Old style callback is deprecated.  See: {link}')
     # Most of legacy advanced options becomes callbacks
     if early_stopping_rounds is not None:
         callbacks.append(callback.early_stop(early_stopping_rounds,
@@ -85,7 +87,7 @@ def _train_internal(params, dtrain,
     is_new_callback = _is_new_callback(callbacks)
     if is_new_callback:
         assert all(isinstance(c, callback.TrainingCallback)
-                   for c in callbacks), "You can't mix two styles of callbacks."
+                   for c in callbacks), "You can't mix new and old callback styles."
         if verbose_eval:
             callbacks.append(callback.EvaluationMonitor())
         if early_stopping_rounds:
@@ -478,7 +480,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
     is_new_callback = _is_new_callback(callbacks)
     if is_new_callback:
         assert all(isinstance(c, callback.TrainingCallback)
-                   for c in callbacks), "You can't mix two styles of callbacks."
+                   for c in callbacks), "You can't mix new and old callback styles."
         if isinstance(verbose_eval, bool) and verbose_eval:
             callbacks.append(callback.EvaluationMonitor(show_stdv=show_stdv))
         if early_stopping_rounds:

tests/python/test_callback.py

@@ -74,7 +74,7 @@ def test_early_stopping_customize(self):
         D_valid = xgb.DMatrix(self.X_valid, self.y_valid)
         early_stopping_rounds = 5
         early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
-                                                metric_name='PyError',
+                                                metric_name='CustomErr',
                                                 data_name='Train')
         # Specify which dataset and which metric should be used for early stopping.
         booster = xgb.train(
@@ -88,7 +88,7 @@ def test_early_stopping_customize(self):
             verbose_eval=False)
         dump = booster.get_dump(dump_format='json')
         assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
-        assert len(early_stop.stopping_history['Train']['PyError']) == len(dump)
+        assert len(early_stop.stopping_history['Train']['CustomErr']) == len(dump)
 
     def test_early_stopping_skl(self):
         from sklearn.datasets import load_breast_cancer