[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 @@ def __init__(self, params=None, cache=(), model_file=None):
         _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 @@ def __init__(self, params=None, cache=(), model_file=None):
         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

tests/python/test_basic.py

@@ -57,6 +57,25 @@ def test_basic(self):
             # assert they are the same
             assert np.sum(np.abs(preds2 - preds)) == 0
 
+    def test_metric_config(self):
+        # Make sure that the metric configuration happens in booster so the
+        # string `['error', 'auc']` doesn't get passed down to core.
+        dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
+        dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
+        param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
+                 'objective': 'binary:logistic', 'eval_metric': ['error', 'auc']}
+        watchlist = [(dtest, 'eval'), (dtrain, 'train')]
+        num_round = 2
+        booster = xgb.train(param, dtrain, num_round, watchlist)
+        predt_0 = booster.predict(dtrain)
+        with tempfile.TemporaryDirectory() as tmpdir:
+            path = os.path.join(tmpdir, 'model.json')
+            booster.save_model(path)
+
+            booster = xgb.Booster(params=param, model_file=path)
+            predt_1 = booster.predict(dtrain)
+            np.testing.assert_allclose(predt_0, predt_1)
+
     def test_record_results(self):
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
         dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
@@ -124,8 +143,8 @@ def test_dump(self):
 
         dump2 = bst.get_dump(with_stats=True)
         assert dump2[0].count('\n') == 3, 'Expected 1 root and 2 leaves - 3 lines in dump.'
-        assert (dump2[0].find('\n') > dump1[0].find('\n'),
-                'Expected more info when with_stats=True is given.')
+        msg = 'Expected more info when with_stats=True is given.'
+        assert dump2[0].find('\n') > dump1[0].find('\n'), msg
 
         dump3 = bst.get_dump(dump_format="json")
         dump3j = json.loads(dump3[0])
@@ -248,13 +267,11 @@ def test_DMatrix_save_to_path(self):
         assert binary_path.exists()
         Path.unlink(binary_path)
 
-
     def test_Booster_init_invalid_path(self):
         """An invalid model_file path should raise XGBoostError."""
         with pytest.raises(xgb.core.XGBoostError):
             xgb.Booster(model_file=Path("invalidpath"))
 
-
     def test_Booster_save_and_load(self):
         """Saving and loading model files from paths."""
         save_path = Path("saveload.model")