Merge branch 'master' into master

R-package/DESCRIPTION

@@ -1,7 +1,7 @@
 Package: xgboost
 Type: Package
 Title: Extreme Gradient Boosting
-Version: 0.4-2
+Version: 0.4-3
 Date: 2015-08-01
 Author: Tianqi Chen <[email protected]>, Tong He <[email protected]>,
     Michael Benesty <[email protected]>

R-package/src/xgboost_R.h

@@ -7,11 +7,11 @@
 #ifndef XGBOOST_R_H_ // NOLINT(*)
 #define XGBOOST_R_H_ // NOLINT(*)
 
-extern "C" {
+
 #include <Rinternals.h>
 #include <R_ext/Random.h>
 #include <Rmath.h>
-}
+
 #include <xgboost/c_api.h>
 
 /*!

python-package/xgboost/sklearn.py

@@ -389,6 +389,8 @@ def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
         else:
             evals = ()
 
+        self._features_count = X.shape[1]
+
         self._le = LabelEncoder().fit(y)
         training_labels = self._le.transform(y)
 
@@ -477,6 +479,22 @@ def evals_result(self):
 
         return evals_result
 
+    @property
+    def feature_importances_(self):
+        """
+        Returns
+        -------
+        feature_importances_ : array of shape = [n_features]
+
+        """
+        fs = self.booster().get_fscore()
+        keys = [int(k.replace('f', '')) for k in fs.keys()]
+        fs_dict = dict(zip(keys, fs.values()))
+        all_features_dict = dict.fromkeys(range(0, self._features_count), 0)
+        all_features_dict.update(fs_dict)
+        return np.array(all_features_dict.values())
+
+
 class XGBRegressor(XGBModel, XGBRegressorBase):
     # pylint: disable=missing-docstring
     __doc__ = """Implementation of the scikit-learn API for XGBoost regression.