[tests] fixed codestyle, removed unused code and added several new checks (#1688)

* break huge lines in sklearn tests

* break huge line in plotting tests

* break huge lines in basic tests

* multiple enhancements in engine tests

* multiple enhancements in sklearn tests

* hotfixes

* break huge lines and use with statement in C API test

* make NDCG test more strict

tests/c_api_test/test_.py

@@ -75,7 +75,7 @@ def load_from_file(filename, reference):
     LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data))
     num_feature = ctypes.c_long()
     LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature))
-    print('#data:%d #feature:%d' % (num_data.value, num_feature.value))
+    print('#data: %d #feature: %d' % (num_data.value, num_feature.value))
     return handle
 
 
@@ -86,11 +86,10 @@ def save_to_binary(handle, filename):
 def load_from_csr(filename, reference):
     data = []
     label = []
-    inp = open(filename, 'r')
-    for line in inp.readlines():
-        data.append([float(x) for x in line.split('\t')[1:]])
-        label.append(float(line.split('\t')[0]))
-    inp.close()
+    with open(filename, 'r') as inp:
+        for line in inp.readlines():
+            data.append([float(x) for x in line.split('\t')[1:]])
+            label.append(float(line.split('\t')[0]))
     mat = np.array(data)
     label = np.array(label, dtype=np.float32)
     csr = sparse.csr_matrix(mat)
@@ -116,18 +115,17 @@ def load_from_csr(filename, reference):
     num_feature = ctypes.c_long()
     LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature))
     LIB.LGBM_DatasetSetField(handle, c_str('label'), c_array(ctypes.c_float, label), len(label), 0)
-    print('#data:%d #feature:%d' % (num_data.value, num_feature.value))
+    print('#data: %d #feature: %d' % (num_data.value, num_feature.value))
     return handle
 
 
 def load_from_csc(filename, reference):
     data = []
     label = []
-    inp = open(filename, 'r')
-    for line in inp.readlines():
-        data.append([float(x) for x in line.split('\t')[1:]])
-        label.append(float(line.split('\t')[0]))
-    inp.close()
+    with open(filename, 'r') as inp:
+        for line in inp.readlines():
+            data.append([float(x) for x in line.split('\t')[1:]])
+            label.append(float(line.split('\t')[0]))
     mat = np.array(data)
     label = np.array(label, dtype=np.float32)
     csr = sparse.csc_matrix(mat)
@@ -153,18 +151,17 @@ def load_from_csc(filename, reference):
     num_feature = ctypes.c_long()
     LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature))
     LIB.LGBM_DatasetSetField(handle, c_str('label'), c_array(ctypes.c_float, label), len(label), 0)
-    print('#data:%d #feature:%d' % (num_data.value, num_feature.value))
+    print('#data: %d #feature: %d' % (num_data.value, num_feature.value))
     return handle
 
 
 def load_from_mat(filename, reference):
     data = []
     label = []
-    inp = open(filename, 'r')
-    for line in inp.readlines():
-        data.append([float(x) for x in line.split('\t')[1:]])
-        label.append(float(line.split('\t')[0]))
-    inp.close()
+    with open(filename, 'r') as inp:
+        for line in inp.readlines():
+            data.append([float(x) for x in line.split('\t')[1:]])
+            label.append(float(line.split('\t')[0]))
     mat = np.array(data)
     data = np.array(mat.reshape(mat.size), copy=False)
     label = np.array(label, dtype=np.float32)
@@ -173,8 +170,8 @@ def load_from_mat(filename, reference):
     if reference is not None:
         ref = reference
 
-    LIB.LGBM_DatasetCreateFromMat(data.ctypes.data_as(
-        ctypes.POINTER(ctypes.c_void_p)),
+    LIB.LGBM_DatasetCreateFromMat(
+        data.ctypes.data_as(ctypes.POINTER(ctypes.c_void_p)),
         dtype_float64,
         mat.shape[0],
         mat.shape[1],
@@ -187,7 +184,7 @@ def load_from_mat(filename, reference):
     num_feature = ctypes.c_long()
     LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature))
     LIB.LGBM_DatasetSetField(handle, c_str('label'), c_array(ctypes.c_float, label), len(label), 0)
-    print('#data:%d #feature:%d' % (num_data.value, num_feature.value))
+    print('#data: %d #feature: %d' % (num_data.value, num_feature.value))
     return handle
 
 
@@ -196,12 +193,16 @@ def free_dataset(handle):
 
 
 def test_dataset():
-    train = load_from_file(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.train'), None)
-    test = load_from_mat(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), train)
+    train = load_from_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                        '../../examples/binary_classification/binary.train'), None)
+    test = load_from_mat(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                      '../../examples/binary_classification/binary.test'), train)
     free_dataset(test)
-    test = load_from_csr(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), train)
+    test = load_from_csr(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                      '../../examples/binary_classification/binary.test'), train)
     free_dataset(test)
-    test = load_from_csc(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), train)
+    test = load_from_csc(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                      '../../examples/binary_classification/binary.test'), train)
     free_dataset(test)
     save_to_binary(train, 'train.binary.bin')
     free_dataset(train)
@@ -210,31 +211,43 @@ def test_dataset():
 
 
 def test_booster():
-    train = load_from_mat(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.train'), None)
-    test = load_from_mat(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), train)
+    train = load_from_mat(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                       '../../examples/binary_classification/binary.train'), None)
+    test = load_from_mat(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                      '../../examples/binary_classification/binary.test'), train)
     booster = ctypes.c_void_p()
-    LIB.LGBM_BoosterCreate(train, c_str("app=binary metric=auc num_leaves=31 verbose=0"), ctypes.byref(booster))
+    LIB.LGBM_BoosterCreate(
+        train,
+        c_str("app=binary metric=auc num_leaves=31 verbose=0"),
+        ctypes.byref(booster))
     LIB.LGBM_BoosterAddValidData(booster, test)
     is_finished = ctypes.c_int(0)
     for i in range(1, 101):
         LIB.LGBM_BoosterUpdateOneIter(booster, ctypes.byref(is_finished))
         result = np.array([0.0], dtype=np.float64)
         out_len = ctypes.c_ulong(0)
-        LIB.LGBM_BoosterGetEval(booster, 0, ctypes.byref(out_len), result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
+        LIB.LGBM_BoosterGetEval(
+            booster,
+            0,
+            ctypes.byref(out_len),
+            result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
         if i % 10 == 0:
-            print('%d Iteration test AUC %f' % (i, result[0]))
+            print('%d iteration test AUC %f' % (i, result[0]))
     LIB.LGBM_BoosterSaveModel(booster, 0, -1, c_str('model.txt'))
     LIB.LGBM_BoosterFree(booster)
     free_dataset(train)
     free_dataset(test)
     booster2 = ctypes.c_void_p()
     num_total_model = ctypes.c_long()
-    LIB.LGBM_BoosterCreateFromModelfile(c_str('model.txt'), ctypes.byref(num_total_model), ctypes.byref(booster2))
+    LIB.LGBM_BoosterCreateFromModelfile(
+        c_str('model.txt'),
+        ctypes.byref(num_total_model),
+        ctypes.byref(booster2))
     data = []
-    inp = open(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test'), 'r')
-    for line in inp.readlines():
-        data.append([float(x) for x in line.split('\t')[1:]])
-    inp.close()
+    with open(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                           '../../examples/binary_classification/binary.test'), 'r') as inp:
+        for line in inp.readlines():
+            data.append([float(x) for x in line.split('\t')[1:]])
     mat = np.array(data)
     preb = np.zeros(mat.shape[0], dtype=np.float64)
     num_preb = ctypes.c_long()
@@ -253,7 +266,8 @@ def test_booster():
         preb.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
     LIB.LGBM_BoosterPredictForFile(
         booster2,
-        c_str(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test')),
+        c_str(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                           '../../examples/binary_classification/binary.test')),
         0,
         0,
         50,

tests/python_package_test/test_basic.py

@@ -13,7 +13,8 @@ from sklearn.model_selection import train_test_split
 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, random_state=2)
+        X_train, X_test, y_train, y_test = train_test_split(*load_breast_cancer(True),
+                                                            test_size=0.1, random_state=2)
         train_data = lgb.Dataset(X_train, label=y_train)
         valid_data = train_data.create_valid(X_test, label=y_test)
 
@@ -80,8 +81,10 @@ class TestBasic(unittest.TestCase):
         valid_data.construct()
 
     def test_subset_group(self):
-        X_train, y_train = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.train'))
-        q_train = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.train.query'))
+        X_train, y_train = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                                           '../../examples/lambdarank/rank.train'))
+        q_train = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                          '../../examples/lambdarank/rank.train.query'))
         lgb_train = lgb.Dataset(X_train, y_train, group=q_train)
         self.assertEqual(len(lgb_train.get_group()), 201)
         subset = lgb_train.subset(list(lgb.compat.range_(10))).construct()

tests/python_package_test/test_engine.py

@@ -10,7 +10,7 @@ import random
 import numpy as np
 from sklearn.datasets import (load_boston, load_breast_cancer, load_digits,
                               load_iris, load_svmlight_file)
-from sklearn.metrics import log_loss, mean_absolute_error, mean_squared_error
+from sklearn.metrics import log_loss, mean_absolute_error, mean_squared_error, roc_auc_score
 from sklearn.model_selection import train_test_split, TimeSeriesSplit, GroupKFold
 from scipy.sparse import csr_matrix
 
@@ -25,7 +25,6 @@ def multi_logloss(y_true, y_pred):
 
 
 class TestEngine(unittest.TestCase):
-
     def test_binary(self):
         X, y = load_breast_cancer(True)
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
@@ -145,6 +144,9 @@ class TestEngine(unittest.TestCase):
                         evals_result=evals_result)
         pred = gbm.predict(X_train)
         np.testing.assert_almost_equal(pred, y)
+        ret = roc_auc_score(y_train, pred)
+        self.assertGreater(ret, 0.999)
+        self.assertAlmostEqual(evals_result['valid_0']['auc'][-1], ret, places=5)
 
     def test_missing_value_handle_zero(self):
         x = [0, 1, 2, 3, 4, 5, 6, 7, np.nan]
@@ -174,6 +176,9 @@ class TestEngine(unittest.TestCase):
                         evals_result=evals_result)
         pred = gbm.predict(X_train)
         np.testing.assert_almost_equal(pred, y)
+        ret = roc_auc_score(y_train, pred)
+        self.assertGreater(ret, 0.999)
+        self.assertAlmostEqual(evals_result['valid_0']['auc'][-1], ret, places=5)
 
     def test_missing_value_handle_none(self):
         x = [0, 1, 2, 3, 4, 5, 6, 7, np.nan]
@@ -204,6 +209,9 @@ class TestEngine(unittest.TestCase):
         pred = gbm.predict(X_train)
         self.assertAlmostEqual(pred[0], pred[1], places=5)
         self.assertAlmostEqual(pred[-1], pred[0], places=5)
+        ret = roc_auc_score(y_train, pred)
+        self.assertGreater(ret, 0.83)
+        self.assertAlmostEqual(evals_result['valid_0']['auc'][-1], ret, places=5)
 
     def test_categorical_handle(self):
         x = [0, 1, 2, 3, 4, 5, 6, 7]
@@ -238,8 +246,11 @@ class TestEngine(unittest.TestCase):
                         evals_result=evals_result)
         pred = gbm.predict(X_train)
         np.testing.assert_almost_equal(pred, y)
+        ret = roc_auc_score(y_train, pred)
+        self.assertGreater(ret, 0.999)
+        self.assertAlmostEqual(evals_result['valid_0']['auc'][-1], ret, places=5)
 
-    def test_categorical_handle2(self):
+    def test_categorical_handle_na(self):
         x = [0, np.nan, 0, np.nan, 0, np.nan]
         y = [0, 1, 0, 1, 0, 1]
 
@@ -272,6 +283,9 @@ class TestEngine(unittest.TestCase):
                         evals_result=evals_result)
         pred = gbm.predict(X_train)
         np.testing.assert_almost_equal(pred, y)
+        ret = roc_auc_score(y_train, pred)
+        self.assertGreater(ret, 0.999)
+        self.assertAlmostEqual(evals_result['valid_0']['auc'][-1], ret, places=5)
 
     def test_multiclass(self):
         X, y = load_digits(10, True)
@@ -331,20 +345,19 @@ class TestEngine(unittest.TestCase):
             'verbose': -1
         }
         lgb_train = lgb.Dataset(X_train, y_train, params=params)
-        lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, params=params)
-        evals_result = {}
         gbm = lgb.train(params, lgb_train,
-                        num_boost_round=50,
-                        valid_sets=lgb_eval,
-                        verbose_eval=False,
-                        evals_result=evals_result)
+                        num_boost_round=50)
 
-        pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 1.5}
+        pred_parameter = {"pred_early_stop": True,
+                          "pred_early_stop_freq": 5,
+                          "pred_early_stop_margin": 1.5}
         ret = multi_logloss(y_test, gbm.predict(X_test, **pred_parameter))
         self.assertLess(ret, 0.8)
         self.assertGreater(ret, 0.5)  # loss will be higher than when evaluating the full model
 
-        pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 5.5}
+        pred_parameter = {"pred_early_stop": True,
+                          "pred_early_stop_freq": 5,
+                          "pred_early_stop_margin": 5.5}
         ret = multi_logloss(y_test, gbm.predict(X_test, **pred_parameter))
         self.assertLess(ret, 0.2)
 
@@ -486,23 +499,23 @@ class TestEngine(unittest.TestCase):
         X_train, _, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42)
         params = {'verbose': -1}
         lgb_train = lgb.Dataset(X_train, y_train)
-        feature_names = ['f_' + str(i) for i in range(13)]
+        feature_names = ['f_' + str(i) for i in range(X_train.shape[-1])]
         gbm = lgb.train(params, lgb_train, num_boost_round=5, feature_name=feature_names)
         self.assertListEqual(feature_names, gbm.feature_name())
         # test feature_names with whitespaces
-        feature_names_with_space = ['f ' + str(i) for i in range(13)]
+        feature_names_with_space = ['f ' + str(i) for i in range(X_train.shape[-1])]
         gbm = lgb.train(params, lgb_train, num_boost_round=5, feature_name=feature_names_with_space)
         self.assertListEqual(feature_names, gbm.feature_name())
 
     def test_save_load_copy_pickle(self):
         def test_template(init_model=None, return_model=False):
             X, y = load_boston(True)
+            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
             params = {
                 'objective': 'regression',
                 'metric': 'l2',
                 'verbose': -1
             }
-            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)
             gbm_template = lgb.train(params, lgb_train, num_boost_round=10, init_model=init_model)
             return gbm_template if return_model else mean_squared_error(y_test, gbm_template.predict(X_test))
@@ -576,7 +589,11 @@ class TestEngine(unittest.TestCase):
         tmp_dat_train = tmp_dat.subset(np.arange(80))
         tmp_dat_val = tmp_dat.subset(np.arange(80, 100)).subset(np.arange(18))
         params = {'objective': 'regression_l2', 'metric': 'rmse'}
-        gbm = lgb.train(params, tmp_dat_train, num_boost_round=20, valid_sets=[tmp_dat_train, tmp_dat_val])
+        evals_result = {}
+        gbm = lgb.train(params, tmp_dat_train, num_boost_round=20,
+                        valid_sets=[tmp_dat_train, tmp_dat_val], evals_result=evals_result)
+        self.assertEqual(len(evals_result['training']['rmse']), 20)
+        self.assertEqual(len(evals_result['valid_1']['rmse']), 20)
 
     def test_contribs(self):
         X, y = load_breast_cancer(True)
@@ -587,15 +604,11 @@ class TestEngine(unittest.TestCase):
             'verbose': -1,
         }
         lgb_train = lgb.Dataset(X_train, y_train)
-        lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
-        evals_result = {}
         gbm = lgb.train(params, lgb_train,
-                        num_boost_round=20,
-                        valid_sets=lgb_eval,
-                        verbose_eval=False,
-                        evals_result=evals_result)
+                        num_boost_round=20)
 
-        self.assertLess(np.linalg.norm(gbm.predict(X_test, raw_score=True) - np.sum(gbm.predict(X_test, pred_contrib=True), axis=1)), 1e-4)
+        self.assertLess(np.linalg.norm(gbm.predict(X_test, raw_score=True)
+                                       - np.sum(gbm.predict(X_test, pred_contrib=True), axis=1)), 1e-4)
 
     def test_sliced_data(self):
         def train_and_get_predictions(features, labels):
@@ -605,13 +618,13 @@ class TestEngine(unittest.TestCase):
                 'verbose': -1,
                 'min_data': 5,
             }
-            lgbm_model = lgb.train(
+            gbm = lgb.train(
                 params=lgb_params,
                 train_set=dataset,
                 num_boost_round=10,
             )
-            predictions = lgbm_model.predict(features)
-            return predictions
+            return gbm.predict(features)
+
         num_samples = 100
         features = np.random.rand(num_samples, 5)
         positive_samples = int(num_samples * 0.25)
@@ -649,10 +662,10 @@ class TestEngine(unittest.TestCase):
 
     def test_monotone_constraint(self):
         def is_increasing(y):
-            return np.count_nonzero(np.diff(y) < 0.0) == 0
+            return (np.diff(y) >= 0.0).all()
 
         def is_decreasing(y):
-            return np.count_nonzero(np.diff(y) > 0.0) == 0
+            return (np.diff(y) <= 0.0).all()
 
         def is_correctly_constrained(learner):
             n = 200
@@ -697,8 +710,7 @@ class TestEngine(unittest.TestCase):
             'min_data': 10
         }
         lgb_train = lgb.Dataset(X_train, y_train)
-        gbm = lgb.train(params, lgb_train,
-                        num_boost_round=20)
+        gbm = lgb.train(params, lgb_train, num_boost_round=20)
         err_pred = log_loss(y_test, gbm.predict(X_test))
         new_gbm = gbm.refit(X_test, y_test)
         new_err_pred = log_loss(y_test, new_gbm.predict(X_test))
@@ -716,8 +728,7 @@ class TestEngine(unittest.TestCase):
             'boost_from_average': False
         }
         lgb_train = lgb.Dataset(X, y)
-        gbm = lgb.train(params, lgb_train,
-                        num_boost_round=20)
+        gbm = lgb.train(params, lgb_train, num_boost_round=20)
         pred = gbm.predict(X)
         pred_mean = pred.mean()
         self.assertGreater(pred_mean, 20)
@@ -734,8 +745,7 @@ class TestEngine(unittest.TestCase):
             'boost_from_average': False
         }
         lgb_train = lgb.Dataset(X, y)
-        gbm = lgb.train(params, lgb_train,
-                        num_boost_round=40)
+        gbm = lgb.train(params, lgb_train, num_boost_round=40)
         pred = gbm.predict(X)
         pred_mean = pred.mean()
         self.assertGreater(pred_mean, 18)

tests/python_package_test/test_plotting.py

@@ -17,7 +17,8 @@ if GRAPHVIZ_INSTALLED:
 class TestBasic(unittest.TestCase):
 
     def setUp(self):
-        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(*load_breast_cancer(True), test_size=0.1, random_state=1)
+        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(*load_breast_cancer(True),
+                                                                                test_size=0.1, random_state=1)
         self.train_data = lgb.Dataset(self.X_train, self.y_train)
         self.params = {
             "objective": "binary",

tests/python_package_test/test_sklearn.py

@@ -57,10 +57,14 @@ class TestSklearn(unittest.TestCase):
         self.assertAlmostEqual(ret, gbm.evals_result_['valid_0']['multi_logloss'][gbm.best_iteration_ - 1], places=5)
 
     def test_lambdarank(self):
-        X_train, y_train = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.train'))
-        X_test, y_test = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.test'))
-        q_train = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.train.query'))
-        q_test = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/lambdarank/rank.test.query'))
+        X_train, y_train = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                                           '../../examples/lambdarank/rank.train'))
+        X_test, y_test = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                                         '../../examples/lambdarank/rank.test'))
+        q_train = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                          '../../examples/lambdarank/rank.train.query'))
+        q_test = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)),
+                                         '../../examples/lambdarank/rank.test.query'))
         gbm = lgb.LGBMRanker()
         gbm.fit(X_train, y_train, group=q_train, eval_set=[(X_test, y_test)],
                 eval_group=[q_test], eval_at=[1, 3], early_stopping_rounds=5, verbose=False,
@@ -74,6 +78,7 @@ class TestSklearn(unittest.TestCase):
             grad = (y_pred - y_true)
             hess = np.ones(len(y_true))
             return grad, hess
+
         X, y = load_boston(True)
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
         gbm = lgb.LGBMRegressor(n_estimators=50, silent=True, objective=objective_ls)
@@ -88,10 +93,11 @@ class TestSklearn(unittest.TestCase):
             grad = y_pred - y_true
             hess = y_pred * (1.0 - y_pred)
             return grad, hess
-        X, y = load_digits(2, True)
 
         def binary_error(y_test, y_pred):
             return np.mean([int(p > 0.5) != y for y, p in zip(y_test, y_pred)])
+
+        X, y = load_digits(2, True)
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
         gbm = lgb.LGBMClassifier(n_estimators=50, silent=True, objective=logregobj)
         gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)