refine callback (#223)

python-package/lightgbm/callback.py

@@ -3,6 +3,7 @@
 from __future__ import absolute_import
 
 import collections
+from operator import gt, lt
 
 from .compat import range_
 
@@ -159,48 +160,50 @@ def early_stopping(stopping_rounds, verbose=True):
     callback : function
         The requested callback function.
     """
-    factor_to_bigger_better = {}
-    best_score = {}
-    best_iter = {}
-    best_msg = {}
+    best_score = []
+    best_iter = []
+    best_msg = []
+    cmp_op = []
 
     def init(env):
         """internal function"""
         if not env.evaluation_result_list:
-            raise ValueError('For early stopping, at least one dataset or eval metric is required for evaluation')
+            raise ValueError('For early stopping, at least one dataset and eval metric is required for evaluation')
 
         if verbose:
             msg = "Train until valid scores didn't improve in {} rounds."
             print(msg.format(stopping_rounds))
 
-        for i in range_(len(env.evaluation_result_list)):
-            best_score[i] = float('-inf')
-            best_iter[i] = 0
+        for eval_ret in env.evaluation_result_list:
+            best_iter.append(0)
             if verbose:
-                best_msg[i] = ""
-            factor_to_bigger_better[i] = 1.0 if env.evaluation_result_list[i][3] else -1.0
+                best_msg.append(None)
+            if eval_ret[3]:
+                best_score.append(float('-inf'))
+                cmp_op.append(gt)
+            else:
+                best_score.append(float('inf'))
+                cmp_op.append(lt)
 
     def callback(env):
         """internal function"""
-        if not best_score:
+        if not cmp_op:
             init(env)
+        best_msg_buffer = None
         for i in range_(len(env.evaluation_result_list)):
-            score = env.evaluation_result_list[i][2] * factor_to_bigger_better[i]
-            if score > best_score[i]:
+            score = env.evaluation_result_list[i][2]
+            if cmp_op[i](score, best_score[i]):
                 best_score[i] = score
                 best_iter[i] = env.iteration
                 if verbose:
-                    best_msg[i] = '[%d]\t%s' % (
-                        env.iteration + 1, '\t'.join(
-                            [_format_eval_result(x) for x in env.evaluation_result_list]
-                        )
-                    )
-            else:
-                if env.iteration - best_iter[i] >= stopping_rounds:
+                    if not best_msg_buffer:
+                        best_msg_buffer = '[%d]\t%s' % (
+                            env.iteration + 1, '\t'.join([_format_eval_result(x) for x in env.evaluation_result_list]))
+                    best_msg[i] = best_msg_buffer
+            elif env.iteration - best_iter[i] >= stopping_rounds:
                 env.model.set_attr(best_iteration=str(best_iter[i]))
                 if verbose:
-                        print('Early stopping, best iteration is:')
-                        print(best_msg[i])
+                    print('Early stopping, best iteration is:\n' + best_msg[i])
                 raise EarlyStopException(best_iter[i])
     callback.order = 30
     return callback