Merge pull request #200 from ClimbsRocks/master

sklearn compatibility update- renames .feature_importance_ to .featur…

docs/Python-API.md

@@ -796,7 +796,7 @@ The methods of each Class is in alphabetical order.
 
     Get the evaluation results.
 
-####feature_importance_
+####feature_importances_
 
     Get normailized feature importances.
 

examples/python-guide/sklearn_example.py

@@ -34,7 +34,7 @@ print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)
 
 print('Calculate feature importances...')
 # feature importances
-print('Feature importances:', list(gbm.feature_importance_))
+print('Feature importances:', list(gbm.feature_importances_))
 
 # other scikit-learn modules
 estimator = lgb.LGBMRegressor(num_leaves=31)

python-package/lightgbm/sklearn.py

@@ -463,7 +463,7 @@ class LGBMModel(LGBMModelBase):
         return self.evals_result
 
     @property
-    def feature_importance_(self):
+    def feature_importances_(self):
         """Get normailized feature importances."""
         importace_array = self.booster_.feature_importance().astype(np.float32)
         return importace_array / importace_array.sum()
@@ -472,9 +472,9 @@ class LGBMModel(LGBMModelBase):
     def booster(self):
         return self.booster_
 
-    @LGBMDeprecated('Use attribute feature_importance_ instead.')
+    @LGBMDeprecated('Use attribute feature_importances_ instead.')
     def feature_importance(self):
-        return self.feature_importance_
+        return self.feature_importances_
 
 
 class LGBMRegressor(LGBMModel, LGBMRegressorBase):

tests/python_package_test/test_sklearn.py

@@ -105,12 +105,12 @@ class TestSklearn(unittest.TestCase):
         gbm = template.test_template(return_model=True)
         gbm_clone = clone(gbm)
         self.assertIsInstance(gbm.booster_, lgb.Booster)
-        self.assertIsInstance(gbm.feature_importance_, np.ndarray)
+        self.assertIsInstance(gbm.feature_importances_, np.ndarray)
         clf = template.test_template(load_digits(2, True), model=lgb.LGBMClassifier, return_model=True)
         self.assertListEqual(sorted(clf.classes_), [0, 1])
         self.assertEqual(clf.n_classes_, 2)
         self.assertIsInstance(clf.booster_, lgb.Booster)
-        self.assertIsInstance(clf.feature_importance_, np.ndarray)
+        self.assertIsInstance(clf.feature_importances_, np.ndarray)
 
     def test_joblib(self):
         gbm = template.test_template(num_round=10, return_model=True)
@@ -118,7 +118,7 @@ class TestSklearn(unittest.TestCase):
         gbm_pickle = joblib.load('lgb.pkl')
         self.assertIsInstance(gbm_pickle.booster_, lgb.Booster)
         self.assertDictEqual(gbm.get_params(), gbm_pickle.get_params())
-        self.assertListEqual(list(gbm.feature_importance_), list(gbm_pickle.feature_importance_))
+        self.assertListEqual(list(gbm.feature_importances_), list(gbm_pickle.feature_importances_))
         X_train, X_test, y_train, y_test = template.test_template(return_data=True)
         gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)
         gbm_pickle.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)