return importance history from fit (#88)

Author: davidfstein

boruta/boruta_py.py

@@ -136,6 +136,10 @@ class BorutaPy(BaseEstimator, TransformerMixin):
         best) features are assigned rank 1 and tentative features are assigned
         rank 2.
 
+    importance_history_ : array-like, shape [n_features, n_iters]
+
+        The calculated importance values for each feature across all iterations.  
+
     Examples
     --------
     
@@ -378,6 +382,8 @@ def _fit(self, X, y):
             # all are selected, thus we set feature supports to True
             self.support_ = np.ones(n_feat, dtype=np.bool)
 
+        self.importance_history_ = imp_history
+
         # notify user
         if self.verbose > 0:
             self._print_results(dec_reg, _iter, 1)