test for early_stopping

python-package/lightgbm/callback.py

@@ -148,8 +148,11 @@ def early_stop(stopping_rounds, verbose=True):
     callback : function
         The requested callback function.
     """
-    is_init = False
-    final_best_iter = 0
+    state = {}
+    factor_to_bigger_better = {}
+    best_score = {}
+    best_iter = {}
+    best_msg = {}
     def init(env):
         """internal function"""
         bst = env.model
@@ -160,19 +163,20 @@ def early_stop(stopping_rounds, verbose=True):
         if verbose:
             msg = "Will train until hasn't improved in {} rounds.\n"
             print(msg.format(stopping_rounds))
-        best_scores = [ float('-inf') for _ in range(len(env.evaluation_result_list))]
-        best_iter = [ 0 for _ in range(len(env.evaluation_result_list))]
-        if verbose:
-            best_msg = [ "" for _ in range(len(env.evaluation_result_list))]
-        factor_to_bigger_better = [-1.0 for _ in range(len(env.evaluation_result_list))]
+
         for i in range(len(env.evaluation_result_list)):
-            if evaluation.evaluation_result_list[i][3]:
+            best_score[i] = float('-inf')
+            best_iter[i] = 0
+            if verbose:
+                best_msg[i] = ""
+            factor_to_bigger_better[i] = -1.0
+            if env.evaluation_result_list[i][3]:
                 factor_to_bigger_better[i] = 1.0
-        is_init = True
+        state['best_iter'] = 0
 
     def callback(env):
         """internal function"""
-        if not is_init:
+        if len(best_score) == 0:
             init(env)
         for i in range(len(env.evaluation_result_list)): 
             score = env.evaluation_result_list[i][2] * factor_to_bigger_better[i]
@@ -184,7 +188,7 @@ def early_stop(stopping_rounds, verbose=True):
                         '\t'.join([_format_eval_result(x) for x in env.evaluation_result_list]))
             else:
                 if env.iteration - best_iter[i] >= stopping_rounds:
-                    final_best_iter = best_iter[i]
+                    state['best_iter'] = best_iter[i]
                     if env.model is not None:
                         env.model.set_attr(best_iteration=str(best_iter[i]))
                     if verbose:

python-package/lightgbm/engine.py

@@ -112,7 +112,7 @@ def train(params, train_data, num_boost_round=100,
     if is_str(init_model):
         predictor = Predictor(model_file=init_model)
     elif isinstance(init_model, Booster):
-        predictor = Booster.to_predictor()
+        predictor = init_model.to_predictor()
     elif isinstance(init_model, Predictor):
         predictor = init_model
     else:
@@ -409,6 +409,6 @@ def cv(params, train_data, num_boost_round=10, nfold=5, stratified=False,
                                evaluation_result_list=res))
         except callback.EarlyStopException as e:
             for k in results.keys():
-                results[k] = results[k][:(e.final_best_iter + 1)]
+                results[k] = results[k][:(e.state['best_iter'] + 1)]
             break
     return results

tests/python_package_test/test_sklearn.py

@@ -96,8 +96,26 @@ def test_binary_classification_with_custom_objective():
           if int(preds[i] > 0.5) != y_test[i]) / float(len(preds))
     assert err < 0.1
 
+def test_early_stopping():
+    from sklearn.metrics import mean_squared_error
+    from sklearn.datasets import load_boston
+    from sklearn.cross_validation import KFold
+    from sklearn import datasets, metrics, model_selection
+
+    boston = load_boston()
+    y = boston['target']
+    X = boston['data']
+    x_train, x_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=1)
+    lgb_model = lgb.LGBMRegressor(n_estimators=500) \
+            .fit(x_train, y_train, eval_set=[(x_test, y_test)], 
+                eval_metric='l2', 
+                early_stopping_rounds=10,
+                verbose=10)
+    print(lgb_model.best_iteration)
+
 test_binary_classification()
 test_multiclass_classification()
 test_regression()
 test_regression_with_custom_objective()
 test_binary_classification_with_custom_objective()
+test_early_stopping()
\ No newline at end of file