Update Python documents.

demo/guide-python/cross_validation.py

@@ -14,15 +14,15 @@
 # std_value is standard deviation of the metric
 xgb.cv(param, dtrain, num_round, nfold=5,
        metrics={'error'}, seed=0,
-       callbacks=[xgb.callback.print_evaluation(show_stdv=True)])
+       callbacks=[xgb.callback.EvaluationMonitor(show_stdv=True)])
 
 print('running cross validation, disable standard deviation display')
 # do cross validation, this will print result out as
 # [iteration]  metric_name:mean_value
 res = xgb.cv(param, dtrain, num_boost_round=10, nfold=5,
              metrics={'error'}, seed=0,
-             callbacks=[xgb.callback.print_evaluation(show_stdv=False),
-                        xgb.callback.early_stop(3)])
+             callbacks=[xgb.callback.EvaluationMonitor(show_stdv=False),
+                        xgb.callback.EarlyStopping(3)])
 print(res)
 print('running cross validation, with preprocessing function')
 # define the preprocessing function

doc/python/python_api.rst

@@ -69,13 +69,15 @@ Plotting API
 
 Callback API
 ------------
-.. autofunction:: xgboost.callback.print_evaluation
+.. autofunction:: xgboost.callback.TrainingCallback
 
-.. autofunction:: xgboost.callback.record_evaluation
+.. autofunction:: xgboost.callback.EvaluationMonitor
 
-.. autofunction:: xgboost.callback.reset_learning_rate
+.. autofunction:: xgboost.callback.EarlyStopping
 
-.. autofunction:: xgboost.callback.early_stop
+.. autofunction:: xgboost.callback.LearningRateScheduler
+
+.. autofunction:: xgboost.callback.TrainingCheckPoint
 
 .. _dask_api:
 
@@ -91,6 +93,8 @@ Dask API
 
 .. autofunction:: xgboost.dask.predict
 
+.. autofunction:: xgboost.dask.inplace_predict
+
 .. autofunction:: xgboost.dask.DaskXGBClassifier
 
 .. autofunction:: xgboost.dask.DaskXGBRegressor

python-package/xgboost/sklearn.py

@@ -510,7 +510,8 @@ def fit(self, X, y, sample_weight=None, base_margin=None,
 
             .. code-block:: python
 
-                [xgb.callback.reset_learning_rate(custom_rates)]
+                callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
+                                                        save_best=True)]
 
         """
         self.n_features_in_ = X.shape[1]
@@ -1249,7 +1250,8 @@ def fit(self, X, y, group, sample_weight=None, base_margin=None,
 
             .. code-block:: python
 
-                [xgb.callback.reset_learning_rate(custom_rates)]
+                callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
+                                                        save_best=True)]
 
         """
         # check if group information is provided

tests/python/test_callback.py

@@ -123,9 +123,11 @@ def test_early_stopping_custom_eval_skl(self):
         X, y = load_breast_cancer(return_X_y=True)
         cls = xgb.XGBClassifier()
         early_stopping_rounds = 5
+        early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
+                                                save_best=True)
         cls.fit(X, y, eval_set=[(X, y)],
-                early_stopping_rounds=early_stopping_rounds,
-                eval_metric=tm.eval_error_metric)
+                eval_metric=tm.eval_error_metric,
+                callbacks=[early_stop])
         booster = cls.get_booster()
         dump = booster.get_dump(dump_format='json')
         assert len(dump) - booster.best_iteration == early_stopping_rounds + 1