diff --git a/README.md b/README.md
index 45e724fab..b4d6ce3a5 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ For more details, please refer to [Features](https://github.com/Microsoft/LightG
 News
 ----
 
-12/02/2012 : Release [python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide issues and feedback.
+12/02/2016 : Release [python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide issues and feedback.
 
 Get Started
 ------------
diff --git a/examples/python-guide/README.md b/examples/python-guide/README.md
new file mode 100644
index 000000000..fd9fd4359
--- /dev/null
+++ b/examples/python-guide/README.md
@@ -0,0 +1,18 @@
+Python Package Example
+=====================
+Here is an example for LightGBM to use python package.
+
+***You should install lightgbm (both c++ and python verion) first.***
+
+For the installation, check the wiki [here](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide).
+
+You also need scikit-learn and pandas to run the examples, but they are not required for the package itself. You can install them with pip:
+```
+pip install -U scikit-learn
+pip install -U pandas
+```
+
+Now you can run examples in this folder, for example:
+```
+python simple_example.py
+```
diff --git a/examples/python-guide/simple_example.py b/examples/python-guide/simple_example.py
index d41304f44..a1442eafc 100644
--- a/examples/python-guide/simple_example.py
+++ b/examples/python-guide/simple_example.py
@@ -62,4 +62,3 @@ model_json = gbm.dump_model()
 
 with open('model.json', 'w+') as f:
     json.dump(model_json, f, indent=4)
-
diff --git a/python-package/lightgbm/__init__.py b/python-package/lightgbm/__init__.py
index 6774b2bde..68a8eb8cc 100644
--- a/python-package/lightgbm/__init__.py
+++ b/python-package/lightgbm/__init__.py
@@ -21,3 +21,4 @@ __version__ = 0.1
 __all__ = ['Dataset', 'Booster',
            'train', 'cv',
            'LGBMModel', 'LGBMRegressor', 'LGBMClassifier', 'LGBMRanker']
+
diff --git a/python-package/lightgbm/engine.py b/python-package/lightgbm/engine.py
index 0cf2d5f44..0de35e99d 100644
--- a/python-package/lightgbm/engine.py
+++ b/python-package/lightgbm/engine.py
@@ -245,13 +245,14 @@ class CVBooster(object):
         return self.booster.eval_valid(feval)
 
 try:
-    try:
-        from sklearn.model_selection import StratifiedKFold
-    except ImportError:
-        from sklearn.cross_validation import StratifiedKFold
+    from sklearn.model_selection import StratifiedKFold
     SKLEARN_StratifiedKFold = True
 except ImportError:
-    SKLEARN_StratifiedKFold = False
+    try:
+        from sklearn.cross_validation import StratifiedKFold
+        SKLEARN_StratifiedKFold = True
+    except ImportError:
+        SKLEARN_StratifiedKFold = False
 
 def _make_n_folds(full_data, nfold, param, seed, fpreproc=None, stratified=False):
     """
diff --git a/src/boosting/gbdt.cpp b/src/boosting/gbdt.cpp
index 7c5e9e68f..e6ec6ec27 100644
--- a/src/boosting/gbdt.cpp
+++ b/src/boosting/gbdt.cpp
@@ -416,17 +416,7 @@ std::string GBDT::DumpModel() const {
     ss << models_[i]->ToJSON();
     ss << "}";
   }
-  ss << "]," << std::endl;
-
-  std::vector<std::pair<size_t, std::string>> pairs = FeatureImportance();
-  ss << "\"feature_importances\":{" << std::endl;
-  for (size_t i = 0; i < pairs.size(); ++i) {
-    if (i > 0) {
-      ss << ",";
-    }
-    ss << "\"" << pairs[i].second << "\":" << pairs[i].first;
-  }
-  ss << "}" << std::endl;
+  ss << "]" << std::endl;
 
   ss << "}" << std::endl;
 
diff --git a/tests/python_package_test/test_basic.py b/tests/python_package_test/test_basic.py
index 055d0b321..7dce36872 100644
--- a/tests/python_package_test/test_basic.py
+++ b/tests/python_package_test/test_basic.py
@@ -3,8 +3,8 @@ import numpy as np
 from sklearn import datasets, metrics, model_selection
 import lightgbm as lgb
 
-X, Y = datasets.make_classification(n_samples=100000, n_features=100, random_state=42)
-x_train, x_test, y_train, y_test = model_selection.train_test_split(X, Y, test_size=0.1, random_state=42)
+X, Y = datasets.make_classification(n_samples=100000, n_features=100)
+x_train, x_test, y_train, y_test = model_selection.train_test_split(X, Y, test_size=0.1)
 
 train_data = lgb.Dataset(x_train, max_bin=255, label=y_train)
 valid_data = train_data.create_valid(x_test, label=y_test)