Support min_delta in early stopping. (#7137)

* Support `min_delta` in early stopping.

* Remove abs_tol.

tests/python/test_callback.py

@@ -126,26 +126,30 @@ class TestCallbacks:
         assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
         assert len(early_stop.stopping_history['Train']['CustomErr']) == len(dump)
 
-        # test tolerance, early stop won't occur with high tolerance.
-        tol = 10
         rounds = 100
         early_stop = xgb.callback.EarlyStopping(
             rounds=early_stopping_rounds,
             metric_name='CustomErr',
             data_name='Train',
-            abs_tol=tol
+            min_delta=100,
+            save_best=True,
         )
         booster = xgb.train(
-            {'objective': 'binary:logistic',
-             'eval_metric': ['error', 'rmse'],
-             'tree_method': 'hist'}, D_train,
+            {
+                'objective': 'binary:logistic',
+                'eval_metric': ['error', 'rmse'],
+                'tree_method': 'hist'
+            },
+            D_train,
             evals=[(D_train, 'Train'), (D_valid, 'Valid')],
             feval=tm.eval_error_metric,
             num_boost_round=rounds,
             callbacks=[early_stop],
-            verbose_eval=False)
-        # 0 based index
-        assert booster.best_iteration == rounds - 1
+            verbose_eval=False
+        )
+        # No iteration can be made with min_delta == 100
+        assert booster.best_iteration == 0
+        assert booster.num_boosted_rounds() == 1
 
     def test_early_stopping_skl(self):
         from sklearn.datasets import load_breast_cancer