Fix parameter loading with training continuation. (#7121)

* Add a demo for training continuation.

tests/python/test_demos.py

@@ -127,6 +127,12 @@ def test_callbacks_demo():
     subprocess.check_call(cmd)
 
 
+def test_continuation_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'continuation.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
 # gpu_acceleration is not tested due to covertype dataset is being too huge.
 # gamma regression is not tested as it requires running a R script first.
 # aft viz is not tested due to ploting is not controled

tests/python/test_training_continuation.py

@@ -3,6 +3,8 @@ import testing as tm
 import numpy as np
 import pytest
 import os
+import tempfile
+
 
 rng = np.random.RandomState(1337)
 
@@ -145,3 +147,21 @@ class TestTrainingContinuation:
         for p in params:
             p['updater'] = updaters
         self.run_training_continuation(params[0], params[1], params[2])
+
+    @pytest.mark.skipif(**tm.no_sklearn())
+    def test_changed_parameter(self):
+        from sklearn.datasets import load_breast_cancer
+        X, y = load_breast_cancer(return_X_y=True)
+        clf = xgb.XGBClassifier(n_estimators=2, use_label_encoder=False)
+        clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss")
+        assert tm.non_increasing(clf.evals_result()["validation_0"]["logloss"])
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            clf.save_model(os.path.join(tmpdir, "clf.json"))
+            loaded = xgb.XGBClassifier(use_label_encoder=False)
+            loaded.load_model(os.path.join(tmpdir, "clf.json"))
+
+        clf = xgb.XGBClassifier(n_estimators=2, use_label_encoder=False)
+        # change metric to error
+        clf.fit(X, y, eval_set=[(X, y)], eval_metric="error")
+        assert tm.non_increasing(clf.evals_result()["validation_0"]["error"])