[backport] Fix best_ntree_limit for dart and gblinear. (#6579) (#6587)

* [backport] Fix `best_ntree_limit` for dart and gblinear. (#6579)

* Backport num group test fix.

tests/python/test_training_continuation.py

@@ -123,13 +123,13 @@ class TestTrainingContinuation:
         gbdt_05 = xgb.train(xgb_params_03, dtrain_5class,
                             num_boost_round=7)
         assert gbdt_05.best_ntree_limit == (
-            gbdt_05.best_iteration + 1) * self.num_parallel_tree
+            gbdt_05.best_iteration + 1) * self.num_parallel_tree * 5
         gbdt_05 = xgb.train(xgb_params_03,
                             dtrain_5class,
                             num_boost_round=3,
                             xgb_model=gbdt_05)
         assert gbdt_05.best_ntree_limit == (
-            gbdt_05.best_iteration + 1) * self.num_parallel_tree
+            gbdt_05.best_iteration + 1) * self.num_parallel_tree * 5
 
         res1 = gbdt_05.predict(dtrain_5class)
         res2 = gbdt_05.predict(dtrain_5class,

tests/python/test_with_sklearn.py

@@ -78,6 +78,34 @@ def test_multiclass_classification():
         check_pred(preds4, labels, output_margin=False)
 
 
+def test_best_ntree_limit():
+    from sklearn.datasets import load_iris
+
+    X, y = load_iris(return_X_y=True)
+
+    def train(booster, forest):
+        rounds = 4
+        cls = xgb.XGBClassifier(
+            n_estimators=rounds, num_parallel_tree=forest, booster=booster
+        ).fit(
+            X, y, eval_set=[(X, y)], early_stopping_rounds=3
+        )
+
+        if forest:
+            assert cls.best_ntree_limit == rounds * forest * cls.n_classes_
+        else:
+            assert cls.best_ntree_limit == 0
+
+        # best_ntree_limit is used by default, assert that under gblinear it's
+        # automatically ignored due to being 0.
+        cls.predict(X)
+
+    num_parallel_tree = 4
+    train('gbtree', num_parallel_tree)
+    train('dart', num_parallel_tree)
+    train('gblinear', None)
+
+
 def test_ranking():
     # generate random data
     x_train = np.random.rand(1000, 10)