Define best_iteration only if early stopping is used. (#9403)

* Define `best_iteration` only if early stopping is used.

This is the behavior specified by the document but not honored in the actual code.

- Don't set the attributes if there's no early stopping.
- Clean up the code for callbacks, and replace assertions with proper exceptions.
- Assign the attributes when early stopping `save_best` is used.
- Turn the attributes into Python properties.

---------

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>

demo/guide-python/callbacks.py

@@ -1,9 +1,9 @@
-'''
+"""
 Demo for using and defining callback functions
 ==============================================
 
     .. versionadded:: 1.3.0
-'''
+"""
 import argparse
 import os
 import tempfile
@@ -17,10 +17,11 @@ import xgboost as xgb
 
 
 class Plotting(xgb.callback.TrainingCallback):
-    '''Plot evaluation result during training.  Only for demonstration purpose as it's quite
+    """Plot evaluation result during training.  Only for demonstration purpose as it's quite
     slow to draw.
 
-    '''
+    """
+
     def __init__(self, rounds):
         self.fig = plt.figure()
         self.ax = self.fig.add_subplot(111)
@@ -31,16 +32,16 @@ class Plotting(xgb.callback.TrainingCallback):
         plt.ion()
 
     def _get_key(self, data, metric):
-        return f'{data}-{metric}'
+        return f"{data}-{metric}"
 
     def after_iteration(self, model, epoch, evals_log):
-        '''Update the plot.'''
+        """Update the plot."""
         if not self.lines:
             for data, metric in evals_log.items():
                 for metric_name, log in metric.items():
                     key = self._get_key(data, metric_name)
                     expanded = log + [0] * (self.rounds - len(log))
-                    self.lines[key],  = self.ax.plot(self.x, expanded, label=key)
+                    (self.lines[key],) = self.ax.plot(self.x, expanded, label=key)
                     self.ax.legend()
         else:
             # https://pythonspot.com/matplotlib-update-plot/
@@ -55,8 +56,8 @@ class Plotting(xgb.callback.TrainingCallback):
 
 
 def custom_callback():
-    '''Demo for defining a custom callback function that plots evaluation result during
-    training.'''
+    """Demo for defining a custom callback function that plots evaluation result during
+    training."""
     X, y = load_breast_cancer(return_X_y=True)
     X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)
 
@@ -69,15 +70,16 @@ def custom_callback():
     # Pass it to the `callbacks` parameter as a list.
     xgb.train(
         {
-            'objective': 'binary:logistic',
-            'eval_metric': ['error', 'rmse'],
-            'tree_method': 'hist',
+            "objective": "binary:logistic",
+            "eval_metric": ["error", "rmse"],
+            "tree_method": "hist",
             "device": "cuda",
         },
         D_train,
-        evals=[(D_train, 'Train'), (D_valid, 'Valid')],
+        evals=[(D_train, "Train"), (D_valid, "Valid")],
         num_boost_round=num_boost_round,
-        callbacks=[plotting])
+        callbacks=[plotting],
+    )
 
 
 def check_point_callback():
@@ -90,10 +92,10 @@ def check_point_callback():
             if i == 0:
                 continue
             if as_pickle:
-                path = os.path.join(tmpdir, 'model_' + str(i) + '.pkl')
+                path = os.path.join(tmpdir, "model_" + str(i) + ".pkl")
             else:
-                path = os.path.join(tmpdir, 'model_' + str(i) + '.json')
-            assert(os.path.exists(path))
+                path = os.path.join(tmpdir, "model_" + str(i) + ".json")
+            assert os.path.exists(path)
 
     X, y = load_breast_cancer(return_X_y=True)
     m = xgb.DMatrix(X, y)
@@ -101,31 +103,36 @@ def check_point_callback():
     with tempfile.TemporaryDirectory() as tmpdir:
         # Use callback class from xgboost.callback
         # Feel free to subclass/customize it to suit your need.
-        check_point = xgb.callback.TrainingCheckPoint(directory=tmpdir,
-                                                      iterations=rounds,
-                                                      name='model')
-        xgb.train({'objective': 'binary:logistic'}, m,
-                  num_boost_round=10,
-                  verbose_eval=False,
-                  callbacks=[check_point])
+        check_point = xgb.callback.TrainingCheckPoint(
+            directory=tmpdir, iterations=rounds, name="model"
+        )
+        xgb.train(
+            {"objective": "binary:logistic"},
+            m,
+            num_boost_round=10,
+            verbose_eval=False,
+            callbacks=[check_point],
+        )
         check(False)
 
         # This version of checkpoint saves everything including parameters and
         # model.  See: doc/tutorials/saving_model.rst
-        check_point = xgb.callback.TrainingCheckPoint(directory=tmpdir,
-                                                      iterations=rounds,
-                                                      as_pickle=True,
-                                                      name='model')
-        xgb.train({'objective': 'binary:logistic'}, m,
-                  num_boost_round=10,
-                  verbose_eval=False,
-                  callbacks=[check_point])
+        check_point = xgb.callback.TrainingCheckPoint(
+            directory=tmpdir, iterations=rounds, as_pickle=True, name="model"
+        )
+        xgb.train(
+            {"objective": "binary:logistic"},
+            m,
+            num_boost_round=10,
+            verbose_eval=False,
+            callbacks=[check_point],
+        )
         check(True)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--plot', default=1, type=int)
+    parser.add_argument("--plot", default=1, type=int)
     args = parser.parse_args()
 
     check_point_callback()