bug fix (exception handling)

data/maker/__init__.py

@@ -22,10 +22,12 @@ from multiprocessing import Process, RLock
 from datetime import datetime, timedelta
 
 class Learner(Process):
+
     def __init__(self,**_args):
         
         
         super(Learner, self).__init__() 
+        self.ndx = 0
         if 'gpu' in _args :
             
             os.environ['CUDA_VISIBLE_DEVICES'] = str(_args['gpu'])
@@ -49,19 +51,22 @@ class Learner(Process):
         self._encoder = None
         self._map = None
         self._df = _args['data'] if 'data' in _args else None
-        self.name   =  self.__class__.__name__+'::'+self.info['context']+'::'+self.info['from']
+        self.name   =  self.__class__.__name__+'::'+self.info['from']
+        self.name = self.name.replace('?','')
         #
         # @TODO: allow for verbose mode so we have a sens of what is going on within the newtork
         #
         
-        _log = {'module':self.name,'action':'init','context':self.info['context'],'gpu':(self.gpu if self.gpu is not None else -1)}
+        _log = {'action':'init','context':self.info['context'],'gpu':(self.gpu if self.gpu is not None else -1)}
         self.log(**_log)
 
         # self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
         # sel.max_epoc
     def log(self,**_args):
         logger = transport.factory.instance(**self.store['logger']) if 'logger' in self.store else transport.factory.instance(provider='console',context='write',lock=True)
+        _args = dict({'ndx':self.ndx,'module':self.name,'info':self.info['context'],**_args})
         logger.write(_args)
+        self.ndx += 1
         if hasattr(logger,'close') :
             logger.close()
 
@@ -85,7 +90,7 @@ class Learner(Process):
             _args['map'] = self._map
         self._encoder = prepare.Input(**_args)  if self._df.shape[0] > 0 else None     
         
-        _log = {'module':self.name,'action':'data-prep','input':{'rows':self._df.shape[0],'cols':self._df.shape[1]} }
+        _log = {'action':'data-prep','input':{'rows':self._df.shape[0],'cols':self._df.shape[1]} }
         self.log(**_log)
 class Trainer(Learner):
     """
@@ -134,7 +139,7 @@ class Trainer(Learner):
         # g.run() 
         
         end = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-        _logs = {'module':self.name,'action':'train','input':{'start':beg,'end':end}}
+        _logs = {'action':'train','input':{'start':beg,'end':end,"unique_counts":self._encoder._io[0]}}
         self.log(**_logs)
         self.generate = g
         if self.autopilot :
@@ -180,7 +185,7 @@ class Generator (Learner):
         _candidates= [ self._encoder.revert(matrix=_item) for _item in _iomatrix]
         
         _size = np.sum([len(_item) for _item in _iomatrix])
-        _log = {'module':self.name,'action':'io-data','input':{'candidates':len(_candidates),'rows':int(_size)}}
+        _log = {'action':'io-data','input':{'candidates':len(_candidates),'rows':int(_size)}}
         self.log(**_log)
         self.post(_candidates)
     def approximate(self,_df):
@@ -195,7 +200,7 @@ class Generator (Learner):
             batches = np.array_split(_df[name].fillna(np.nan).values,BATCH_SIZE)
             _type = np.int64 if 'int' in self.info['approximate'][name]else np.float64
             x = []
-            _log = {'module':self.name,'action':'approximate','input':{'batch':BATCH_SIZE,'col':name}}
+            _log = {'action':'approximate','input':{'batch':BATCH_SIZE,'col':name}}
             for values in batches :
                
                 index = [ _x not in ['',None,np.nan] for _x in values]
@@ -285,7 +290,7 @@ class Generator (Learner):
             _df = self.format(_df,_schema)
             writer.write(_df,schema=_schema)
         
-        self.log(**{'module':self.name,'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
+        self.log(**{'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
 class factory :
     _infocache = {}
     @staticmethod

data/maker/prepare/__init__.py

@@ -90,11 +90,14 @@ class Input :
         # else:
         #
         # We will look into the count and make a judgment call
+        try:
             _df = pd.DataFrame(self.df.apply(lambda col: col.dropna().unique().size )).T
             MIN_SPACE_SIZE = 2
             self._columns  = cols if cols else _df.apply(lambda col:None if col[0] == row_count or col[0] < MIN_SPACE_SIZE else col.name).dropna().tolist()
             self._io = _df.to_dict(orient='records')
-            
+        except Exception as e:
+            print (e)
+            self._io = []
     def _initdata(self,**_args):
         """
         This function will initialize the class with a data-frame and columns of interest (if any)