early stopping for Python wrapper

wrapper/xgboost.py

@@ -1,7 +1,10 @@
+# coding: utf-8
+
 """
 xgboost: eXtreme Gradient Boosting library
 
 Authors: Tianqi Chen, Bing Xu
+Early stopping by Zygmunt Zając
 """
 
 from __future__ import absolute_import
@@ -529,6 +532,8 @@ 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.
@@ -541,16 +546,73 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None):
     -------
     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])
-    for i in range(num_boost_round):
-        bst.update(dtrain, i, obj)
-        if len(evals) != 0:
+    
+    if num_boost_round >= 0:
+        for i in range(num_boost_round):
+            bst.update(dtrain, i, obj)
+            if len(evals) != 0:
+                bst_eval_set = bst.eval_set(evals, i, feval)
+                if isinstance(bst_eval_set, string_types):
+                    sys.stderr.write(bst_eval_set + '\n')
+                else:
+                    sys.stderr.write(bst_eval_set.decode() + '\n')
+    else:
+        # early stopping
+        
+        # TODO: return model from the best iteration
+        sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(evals[-1][1], -num_boost_round))
+        
+        # is params a list of tuples? are we using multiple eval metrics?
+        if type(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)
+
+        # either minimize loss or maximize AUC/MAP/NDCG
+        maximize_score = False
+        if 'eval_metric' in params:
+			maximize_metrics = ('auc', 'map', 'ndcg')
+			if filter( lambda x: params['eval_metric'].startswith(x), maximize_metrics ):
+                maximize_score = True
+        
+        if maximize_score:
+            best_score = 0.0
+        else:
+            best_score = float('inf')
+            
+        best_msg = '' 
+        best_score_i = 0
+        i = 0
+        
+        while True:
+            bst.update(dtrain, i, obj)
             bst_eval_set = bst.eval_set(evals, i, feval)
+            
             if isinstance(bst_eval_set, string_types):
-                sys.stderr.write(bst_eval_set + '\n')
+                msg = bst_eval_set
             else:
-                sys.stderr.write(bst_eval_set.decode() + '\n')
+                msg = bst_eval_set.decode()
+                
+            sys.stderr.write(msg + '\n')
+            
+            score = float(msg.rsplit( ':', 1 )[1])
+            if (maximize_score and score > best_score) or \
+                (not maximize_score and score < best_score):
+                best_score = score
+                best_score_i = i
+                best_msg = msg
+            elif i - best_score_i >= -num_boost_round:
+                sys.stderr.write("Stopping. Best iteration:\n{}".format(best_msg))
+                break
+               
+            i += 1
+        
     return bst