early_stopping_rounds for train() in Python wrapper 🔥

wrapper/xgboost.py

@@ -520,7 +520,7 @@ class Booster(object):
         return fmap
 
 
-def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None):
+def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, early_stopping_rounds=None):
     """
     Train a booster with given parameters.
 
@@ -532,28 +532,31 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None):
         Data to be trained.
     num_boost_round: int
         Number of boosting iterations.
-        If negative, train until validation error hasn't decreased in -num_boost_round rounds.
-        Requires at least one item in evals. If there's more than one, will use the last.
     watchlist : list of pairs (DMatrix, string)
         List of items to be evaluated during training, this allows user to watch
         performance on the validation set.
-    obj :  function
+    obj : function
         Customized objective function.
     feval : function
         Customized evaluation function.
+    early_stopping_rounds: int
+        Activates early stopping. Validation error needs to decrease at least
+        every <early_stopping_rounds> round(s) to continue training.
+        Requires at least one item in evals. 
+        If there's more than one, will use the last.
+        Returns the model from the last iteration (not the best one).
+        If early stopping occurs, the model will have two additional fields: 
+        bst.best_score and bst.best_iteration.
 
     Returns
     -------
     booster : a trained booster model
     """
     
-    if num_boost_round < 0 and len(evals) < 1:
-        raise ValueError('For early stopping you need at least on set in evals.')
-    
     evals = list(evals)
     bst = Booster(params, [dtrain] + [d[0] for d in evals])
     
-    if num_boost_round >= 0:
+    if not early_stopping_rounds:
         for i in range(num_boost_round):
             bst.update(dtrain, i, obj)
             if len(evals) != 0:
@@ -562,11 +565,15 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None):
                     sys.stderr.write(bst_eval_set + '\n')
                 else:
                     sys.stderr.write(bst_eval_set.decode() + '\n')
+        return bst
+    
     else:
         # early stopping
         
-        # TODO: return model from the best iteration
+        if len(evals) < 1:
-        sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(evals[-1][1], -num_boost_round))
+            raise ValueError('For early stopping you need at least on set in evals.')        
+        
+        sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(evals[-1][1], early_stopping_rounds))
         
         # is params a list of tuples? are we using multiple eval metrics?
         if type(params) == list:
@@ -588,9 +595,8 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None):
             
         best_msg = '' 
         best_score_i = 0
-        i = 0
         
-        while True:
+        for i in range(num_boost_round):
             bst.update(dtrain, i, obj)
             bst_eval_set = bst.eval_set(evals, i, feval)
             
@@ -607,14 +613,15 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None):
                 best_score = score
                 best_score_i = i
                 best_msg = msg
-            elif i - best_score_i >= -num_boost_round:
+            elif i - best_score_i >= early_stopping_rounds:
-                sys.stderr.write("Stopping. Best iteration:\n{}".format(best_msg))
+                sys.stderr.write("Stopping. Best iteration:\n{}\n\n".format(best_msg))
-                break
+                bst.best_score = best_score
-               
+                bst.best_iteration = best_score_i
-            i += 1
+                return bst
-        
+           
-    return bst
+        return bst
 
+        
 
 class CVPack(object):
     def __init__(self, dtrain, dtest, param):