fix test for continued train, due to default saved number of model is best_iteration now

src/boosting/gbdt.cpp

@@ -17,9 +17,9 @@
 namespace LightGBM {
 
 GBDT::GBDT() 
-  :num_iteration_for_pred_(0), 
+  :iter_(0),
+  num_iteration_for_pred_(0),
   num_init_iteration_(0) {
-
 }
 
 GBDT::~GBDT() {
@@ -581,6 +581,7 @@ void GBDT::LoadModelFromString(const std::string& model_str) {
   Log::Info("Finished loading %d models", models_.size());
   num_iteration_for_pred_ = static_cast<int>(models_.size()) / num_class_;
   num_init_iteration_ = num_iteration_for_pred_;
+  iter_ = 0;
 }
 
 std::vector<std::pair<size_t, std::string>> GBDT::FeatureImportance() const {

tests/python_package_test/test_basic.py

@@ -9,8 +9,7 @@ import lightgbm as lgb
 class TestBasic(unittest.TestCase):
 
     def test(self):
-        X_train, X_test, y_train, y_test = train_test_split(*load_breast_cancer(True), test_size=0.1)
-
+        X_train, X_test, y_train, y_test = train_test_split(*load_breast_cancer(True), test_size=0.1, random_state=1)
         train_data = lgb.Dataset(X_train, max_bin=255, label=y_train)
         valid_data = train_data.create_valid(X_test, label=y_test)
 

tests/python_package_test/test_engine.py

@@ -17,7 +17,7 @@ def multi_logloss(y_true, y_pred):
 def test_template(params = {'objective' : 'regression', 'metric' : 'l2'},
                   X_y=load_boston(True), feval=mean_squared_error,
                   num_round=100, init_model=None, custom_eval=None,
-                  return_data=False, return_model=False):
+                  return_data=False, return_model=False, early_stopping_rounds=10):
     X_train, X_test, y_train, y_test = train_test_split(*X_y, test_size=0.1, random_state=42)
     lgb_train = lgb.Dataset(X_train, y_train, params=params)
     lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, params=params)
@@ -31,7 +31,7 @@ def test_template(params = {'objective' : 'regression', 'metric' : 'l2'},
                     verbose_eval=False,
                     feval=custom_eval,
                     evals_result=evals_result,
-                    early_stopping_rounds=10,
+                    early_stopping_rounds=early_stopping_rounds,
                     init_model=init_model)
     if return_model: return gbm
     else: return evals_result, feval(y_test, gbm.predict(X_test, gbm.best_iteration))
@@ -71,7 +71,7 @@ class TestEngine(unittest.TestCase):
             'metric' : 'l1'
         }
         model_name = 'model.txt'
-        gbm = test_template(params, num_round=20, return_model=True)
+        gbm = test_template(params, num_round=20, return_model=True, early_stopping_rounds=-1)
         gbm.save_model(model_name)
         evals_result, ret = test_template(params, feval=mean_absolute_error,
                                           num_round=80, init_model=model_name,
@@ -91,7 +91,7 @@ class TestEngine(unittest.TestCase):
             'metric' : 'multi_logloss',
             'num_class' : 3
         }
-        gbm = test_template(params, X_y, num_round=20, return_model=True)
+        gbm = test_template(params, X_y, num_round=20, return_model=True, early_stopping_rounds=-1)
         evals_result, ret = test_template(params, X_y, feval=multi_logloss,
                                         num_round=80, init_model=gbm)
         self.assertLess(ret, 1.5)