[backport] Move metric configuration into booster. (#6504) (#6533)

python-package/xgboost/core.py

@@ -1,11 +1,12 @@
 # coding: utf-8
 # pylint: disable=too-many-arguments, too-many-branches, invalid-name
-# pylint: disable=too-many-lines, too-many-locals
+# pylint: disable=too-many-lines, too-many-locals, no-self-use
 """Core XGBoost Library."""
 import collections
 # pylint: disable=no-name-in-module,import-error
 from collections.abc import Mapping
 # pylint: enable=no-name-in-module,import-error
+from typing import Dict, Union, List
 import ctypes
 import os
 import re
@@ -1012,6 +1013,7 @@ class Booster(object):
         _check_call(_LIB.XGBoosterCreate(dmats, c_bst_ulong(len(cache)),
                                          ctypes.byref(self.handle)))
         params = params or {}
+        params = self._configure_metrics(params.copy())
         if isinstance(params, list):
             params.append(('validate_parameters', True))
         else:
@@ -1041,6 +1043,17 @@ class Booster(object):
         else:
             raise TypeError('Unknown type:', model_file)
 
+    def _configure_metrics(self, params: Union[Dict, List]) -> Union[Dict, List]:
+        if isinstance(params, dict) and 'eval_metric' in params \
+           and isinstance(params['eval_metric'], list):
+            params = dict((k, v) for k, v in params.items())
+            eval_metrics = params['eval_metric']
+            params.pop("eval_metric", None)
+            params = list(params.items())
+            for eval_metric in eval_metrics:
+                params += [('eval_metric', eval_metric)]
+        return params
+
     def __del__(self):
         if hasattr(self, 'handle') and self.handle is not None:
             _check_call(_LIB.XGBoosterFree(self.handle))

python-package/xgboost/training.py

@@ -40,18 +40,6 @@ def _is_new_callback(callbacks):
                for c in callbacks) or not callbacks
 
 
-def _configure_metrics(params):
-    if isinstance(params, dict) and 'eval_metric' in params \
-       and isinstance(params['eval_metric'], list):
-        params = dict((k, v) for k, v in params.items())
-        eval_metrics = params['eval_metric']
-        params.pop("eval_metric", None)
-        params = list(params.items())
-        for eval_metric in eval_metrics:
-            params += [('eval_metric', eval_metric)]
-    return params
-
-
 def _train_internal(params, dtrain,
                     num_boost_round=10, evals=(),
                     obj=None, feval=None,
@@ -61,7 +49,6 @@ def _train_internal(params, dtrain,
     """internal training function"""
     callbacks = [] if callbacks is None else copy.copy(callbacks)
     evals = list(evals)
-    params = _configure_metrics(params.copy())
 
     bst = Booster(params, [dtrain] + [d[0] for d in evals])
     nboost = 0