python: multiple eval_metrics changes

- allows feval to return a list of tuples (name, error/score value)
- changed behavior for multiple eval_metrics in conjunction with
early_stopping: Instead of raising an error, the last passed evel_metric
(or last entry in return value of feval) is used for early stopping
- allows list of eval_metrics in dict-typed params
- unittest for new features / behavior

documentation updated

- example for assigning a list to 'eval_metric'
- note about early stopping on last passed eval metric

- info msg for used eval metric added

python-package/xgboost/training.py

@@ -61,6 +61,17 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
     booster : a trained booster model
     """
     evals = list(evals)
+    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)]
+
+    bst = Booster(params, [dtrain] + [d[0] for d in evals])
     ntrees = 0
     if xgb_model is not None:
         if not isinstance(xgb_model, STRING_TYPES):
@@ -70,7 +81,6 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
     else:
         bst = Booster(params, [dtrain] + [d[0] for d in evals])
 
-
     if evals_result is not None:
         if not isinstance(evals_result, dict):
             raise TypeError('evals_result has to be a dictionary')
@@ -120,9 +130,11 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
         # is params a list of tuples? are we using multiple eval metrics?
         if isinstance(params, list):
             if len(params) != len(dict(params).items()):
-                raise ValueError('Check your params.'\
-                                     'Early stopping works with single eval metric only.')
-            params = dict(params)
+                params = dict(params)
+                sys.stderr.write("Multiple eval metrics has been passed: " \
+                "'{0}' will be used for early stopping.\n\n".format(params['eval_metric']))
+            else:
+                params = dict(params)
 
         # either minimize loss or maximize AUC/MAP/NDCG
         maximize_score = False