Fix learning rate scheduler with cv. (#6720)

* Expose more methods in cvpack and packed booster.
* Fix cv context in deprecated callbacks.
* Fix document.

python-package/xgboost/callback.py

@@ -20,6 +20,8 @@ def _get_callback_context(env):
         context = 'train'
     elif env.model is None and env.cvfolds is not None:
         context = 'cv'
+    else:
+        raise ValueError("Unexpected input with both model and cvfolds.")
     return context
 
 
@@ -751,7 +753,7 @@ def before_iteration(self, model, epoch, dtrain, evals):
         '''Called before each iteration.'''
         for cb in self.callbacks_before_iter:
             rank = rabit.get_rank()
-            cb(CallbackEnv(model=model,
+            cb(CallbackEnv(model=None if self.cvfolds is not None else model,
                            cvfolds=self.cvfolds,
                            iteration=epoch,
                            begin_iteration=self.start_iteration,
@@ -764,6 +766,7 @@ def after_iteration(self, model, epoch, dtrain, evals):
         '''Called after each iteration.'''
         evaluation_result_list = []
         if self.cvfolds is not None:
+            # dtrain is not used here.
             scores = model.eval(epoch, self.feval)
             self.aggregated_cv = _aggcv(scores)
             evaluation_result_list = self.aggregated_cv
@@ -782,7 +785,7 @@ def after_iteration(self, model, epoch, dtrain, evals):
         try:
             for cb in self.callbacks_after_iter:
                 rank = rabit.get_rank()
-                cb(CallbackEnv(model=model,
+                cb(CallbackEnv(model=None if self.cvfolds is not None else model,
                                cvfolds=self.cvfolds,
                                iteration=epoch,
                                begin_iteration=self.start_iteration,

python-package/xgboost/training.py

@@ -180,7 +180,7 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
 
         .. code-block:: python
 
-            [xgb.callback.reset_learning_rate(custom_rates)]
+            [xgb.callback.LearningRateScheduler(custom_rates)]
 
     Returns
     -------
@@ -207,6 +207,11 @@ def __init__(self, dtrain, dtest, param):
         self.watchlist = [(dtrain, 'train'), (dtest, 'test')]
         self.bst = Booster(param, [dtrain, dtest])
 
+    def __getattr__(self, name):
+        def _inner(*args, **kwargs):
+            return getattr(self.bst, name)(*args, **kwargs)
+        return _inner
+
     def update(self, iteration, fobj):
         """"Update the boosters for one iteration"""
         self.bst.update(self.dtrain, iteration, fobj)
@@ -239,15 +244,24 @@ def attr(self, key):
         '''Redirect to booster attr.'''
         return self.cvfolds[0].bst.attr(key)
 
+    def set_param(self, params, value=None):
+        """Iterate through folds for set_param"""
+        for f in self.cvfolds:
+            f.bst.set_param(params, value)
+
+    def num_boosted_rounds(self):
+        '''Number of boosted rounds.'''
+        return self.cvfolds[0].num_boosted_rounds()
+
     @property
     def best_iteration(self):
         '''Get best_iteration'''
-        ret = self.cvfolds[0].bst.attr('best_iteration')
-        return int(ret)
+        return int(self.cvfolds[0].bst.attr("best_iteration"))
 
-    def num_boosted_rounds(self) -> int:
-        '''Number of boosted rounds.'''
-        return self.cvfolds[0].bst.num_boosted_rounds()
+    @property
+    def best_score(self):
+        """Get best_score."""
+        return float(self.cvfolds[0].bst.attr("best_score"))
 
 
 def groups_to_rows(groups, boundaries):
@@ -419,7 +433,7 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
 
         .. code-block:: python
 
-            [xgb.callback.reset_learning_rate(custom_rates)]
+            [xgb.callback.LearningRateScheduler(custom_rates)]
     shuffle : bool
         Shuffle data before creating folds.
 

tests/python/test_callback.py

@@ -206,6 +206,7 @@ def test_early_stopping_continuation(self):
                 booster.best_iteration + early_stopping_rounds + 1
 
     def run_eta_decay(self, tree_method, deprecated_callback):
+        """Test learning rate scheduler, used by both CPU and GPU tests."""
         if deprecated_callback:
             scheduler = xgb.callback.reset_learning_rate
         else:
@@ -217,7 +218,10 @@ def run_eta_decay(self, tree_method, deprecated_callback):
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
         num_round = 4
 
-        warning_check = pytest.warns(UserWarning) if deprecated_callback else tm.noop_context()
+        if deprecated_callback:
+            warning_check = pytest.warns(UserWarning)
+        else:
+            warning_check = tm.noop_context()
 
         # learning_rates as a list
         # init eta with 0 to check whether learning_rates work
@@ -288,17 +292,22 @@ def eta_decay(ithround, num_boost_round=num_round):
         for i in range(1, len(eval_errors_0)):
             assert eval_errors_3[i] != eval_errors_2[i]
 
-    def test_eta_decay_hist(self):
-        self.run_eta_decay('hist', True)
-        self.run_eta_decay('hist', False)
-
-    def test_eta_decay_approx(self):
-        self.run_eta_decay('approx', True)
-        self.run_eta_decay('approx', False)
-
-    def test_eta_decay_exact(self):
-        self.run_eta_decay('exact', True)
-        self.run_eta_decay('exact', False)
+        with warning_check:
+            xgb.cv(param, dtrain, num_round, callbacks=[scheduler(eta_decay)])
+
+    @pytest.mark.parametrize(
+        "tree_method, deprecated_callback",
+        [
+            ("hist", True),
+            ("hist", False),
+            ("approx", True),
+            ("approx", False),
+            ("exact", True),
+            ("exact", False),
+        ],
+    )
+    def test_eta_decay(self, tree_method, deprecated_callback):
+        self.run_eta_decay(tree_method, deprecated_callback)
 
     def test_check_point(self):
         from sklearn.datasets import load_breast_cancer