diff --git a/data/__init__.py b/data/__init__.py
index 91b566d..a99964d 100644
--- a/data/__init__.py
+++ b/data/__init__.py
@@ -4,3 +4,4 @@ import transport
 from multiprocessing import Process, Queue
 from data.maker import prepare
 from data.maker import state
+
diff --git a/data/maker/__init__.py b/data/maker/__init__.py
index 894ed65..72706c6 100644
--- a/data/maker/__init__.py
+++ b/data/maker/__init__.py
@@ -76,6 +76,7 @@ class Learner(Process):
         # sel.max_epoc
         self.logger = None
         if 'logger' in self.store :
+            # self.store['logger']['context'] = 'write'
             self.logger = transport.factory.instance(**self.store['logger'])
         self.autopilot = False  #-- to be set by caller
         self._initStateSpace()
@@ -243,7 +244,7 @@ class Trainer(Learner):
             #
             # @TODO Log that the dataset was empty or not statistically relevant
             return 
-        _space,_matrix = self._encoder.convert()
+        _space,_matrix = self._encoder._convert()
         
         _args   = self.network_args
         if self.gpu :
@@ -341,7 +342,7 @@ class Generator (Learner):
         # The values will be returned because we have provided _map information from the constructor
         #
 
-        values,_matrix = self._encoder.convert()
+        values,_matrix = self._encoder._convert()
         _args = self.network_args
         _args['map'] = self._map
         _args['values'] = np.array(values)
@@ -353,7 +354,7 @@ class Generator (Learner):
         gHandler = gan.Predict(**_args)
         gHandler.load_meta(columns=None)
         _iomatrix = gHandler.apply()        
-        _candidates= [ self._encoder.revert(matrix=_item) for _item in _iomatrix]
+        _candidates= [ self._encoder._revert(matrix=_item) for _item in _iomatrix]
         
         _size = np.sum([len(_item) for _item in _iomatrix])
         _log = {'action':'io-data','input':{'candidates':len(_candidates),'rows':int(_size)}}