fix DeprecationWarning on sklearn.cross_validation (#2075)

* fix DeprecationWarning on sklearn.cross_validation

* fix syntax

* fix kfold n_split issue

* fix mistype

* fix n_splits multiple value issue

* split should pass a iterable

* use np.arange instead of xrange, py3 compatibility

demo/guide-python/sklearn_examples.py

@@ -8,6 +8,9 @@ import pickle
 import xgboost as xgb
 
 import numpy as np
+try:
+    from sklearn.model_selection import KFold, train_test_split
+except:
     from sklearn.cross_validation import KFold, train_test_split
 from sklearn.metrics import confusion_matrix, mean_squared_error
 from sklearn.grid_search import GridSearchCV

tests/python/test_early_stopping.py

@@ -11,6 +11,9 @@ class TestEarlyStopping(unittest.TestCase):
     def test_early_stopping_nonparallel(self):
         tm._skip_if_no_sklearn()
         from sklearn.datasets import load_digits
+        try:
+            from sklearn.model_selection import train_test_split
+        except:
             from sklearn.cross_validation import train_test_split
 
         digits = load_digits(2)

tests/python/test_eval_metrics.py

@@ -57,6 +57,9 @@ class TestEvalMetrics(unittest.TestCase):
 
     def test_eval_metrics(self):
         tm._skip_if_no_sklearn()
+        try:
+            from sklearn.model_selection import train_test_split
+        except:
             from sklearn.cross_validation import train_test_split
         from sklearn.datasets import load_digits
 

tests/python/test_fast_hist.py

@@ -10,6 +10,9 @@ class TestFastHist(unittest.TestCase):
     def test_fast_hist(self):
         tm._skip_if_no_sklearn()
         from sklearn.datasets import load_digits
+        try:
+            from sklearn.model_selection import train_test_split
+        except:
             from sklearn.cross_validation import train_test_split
 
         digits = load_digits(2)

tests/python/test_with_sklearn.py

@@ -9,12 +9,20 @@ rng = np.random.RandomState(1994)
 def test_binary_classification():
     tm._skip_if_no_sklearn()
     from sklearn.datasets import load_digits
+    try:
+        from sklearn.model_selection import KFold
+    except:
         from sklearn.cross_validation import KFold
 
     digits = load_digits(2)
     y = digits['target']
     X = digits['data']
+    try:
         kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
+    except TypeError:  # sklearn.model_selection.KFold uses n_split
+        kf = KFold(
+            n_splits=2, shuffle=True, random_state=rng
+        ).split(np.arange(y.shape[0]))
     for train_index, test_index in kf:
         xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])
         preds = xgb_model.predict(X[test_index])
@@ -27,7 +35,10 @@ def test_binary_classification():
 def test_multiclass_classification():
     tm._skip_if_no_sklearn()
     from sklearn.datasets import load_iris
+    try:
         from sklearn.cross_validation import KFold
+    except:
+        from sklearn.model_selection import KFold
 
     def check_pred(preds, labels):
         err = sum(1 for i in range(len(preds))