Update IForest model

pyod/models/iforest.py

@@ -267,6 +267,27 @@ def max_samples_(self):
         Decorator for scikit-learn Isolation Forest attributes.
         """
         return self.detector_.max_samples_
+
+    @property
+    def estimators_features_(self):
+        """The indeces of the subset of features used to train the estimators.
+        Decorator for scikit-learn Isolation Forest attributes.
+        """
+        return self.detector_.estimators_features_
+
+    @property
+    def n_features_in_(self):
+        """The number of features seen during the fit. 
+        Decorator for scikit-learn Isolation Forest attributes.
+        """
+        return self.detector_.n_features_in_
+
+    @property
+    def offset_(self):
+        """Offset used to define the decision function from the raw scores. 
+        Decorator for scikit-learn Isolation Forest attributes.
+        """
+        return self.detector_.offset_
 
     @property
     def feature_importances_(self):

pyod/test/test_iforest.py

@@ -54,6 +54,13 @@ def test_parameters(self):
                 self.clf.estimators_samples_ is not None)
         assert (hasattr(self.clf, 'max_samples_') and
                 self.clf.max_samples_ is not None)
+        assert (hasattr(self.clf, 'estimators_features_') and
+                self.clf.estimators_features_ is not None)
+        assert (hasattr(self.clf, 'n_features_in_') and
+                self.clf.n_features_in_ is not None)
+        assert (hasattr(self.clf, 'offset_') and
+                self.clf.offset_ is not None)
+
 
     def test_train_scores(self):
         assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])