bug fix: logger and io space

data/maker/__init__.py

@@ -28,6 +28,7 @@ class Learner(Process):
         
         super(Learner, self).__init__() 
         self.ndx = 0
+        self.lock = RLock()
         if 'gpu' in _args :
             
             os.environ['CUDA_VISIBLE_DEVICES'] = str(_args['gpu'])
@@ -63,13 +64,21 @@ class Learner(Process):
         # self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
         # sel.max_epoc
     def log(self,**_args):
+        self.lock.acquire()
+        try:
             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,'table':self.info['from'],'info':self.info['context'],**_args})
             logger.write(_args)
             self.ndx += 1
             if hasattr(logger,'close') :
                 logger.close()
-
+        except Exception as e:
+            print ()
+            print (_args)
+            print (e)
+            pass
+        finally:
+            self.lock.release()
     def get_schema(self):
         if self.store['source']['provider'] != 'bigquery' :
             return [{'name':self._df.dtypes.index.tolist()[i],'type':self._df.dtypes.astype(str).tolist()[i]}for i in range(self._df.dtypes.shape[0])]
@@ -89,8 +98,7 @@ class Learner(Process):
         if self._map :
             _args['map'] = self._map
         self._encoder = prepare.Input(**_args)  if self._df.shape[0] > 0 else None             
-        
-        _log = {'action':'data-prep','input':{'rows':self._df.shape[0],'cols':self._df.shape[1]} }
+        _log = {'action':'data-prep','input':{'rows':int(self._df.shape[0]),'cols':int(self._df.shape[1]) } }
         self.log(**_log)
 class Trainer(Learner):
     """
@@ -139,7 +147,7 @@ class Trainer(Learner):
         # g.run() 
         
         end = datetime.now() #.strftime('%Y-%m-%d %H:%M:%S')
-        _min = float(timedelta(end,beg).seconds/ 60)
+        _min = float((end-beg).seconds/ 60)
         _logs = {'action':'train','input':{'start':beg.strftime('%Y-%m-%d %H:%M:%S'),'minutes':_min,"unique_counts":self._encoder._io[0]}}
         self.log(**_logs)
         self.generate = g
@@ -293,12 +301,27 @@ class Generator (Learner):
             writer.write(_df,schema=_schema)
         
         self.log(**{'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
-class Shuffle(Trainer):    
+class Shuffle(Generator):    
     """
     This is a method that will yield data with low utility
     """
     def __init__(self,**_args):
         super().__init__(self)
+    def run(self):
+        
+
+        self.initalize()
+        _index = np.arange(self._df.shape[0])
+        np.random.shuffle(_index)
+        _iocolumns = self.info['columns']
+        _ocolumns = list(set(self._df.columns) - set(_iocolumns) )
+        _iodf = pd.DataFrame(self._df[_ocolumns],self._df.loc[_index][_iocolumns],index=np.arange(self._df.shape[0]))
+        self._df = self._df[_ocolumns].join(_iodf)
+       
+        
+        _log = {'action':'io-data','input':{'candidates':1,'rows':int(self._df.shape[0])}}
+        self.log(**_log)
+        self.post([self._df])
 class factory :
     _infocache = {}
     @staticmethod
@@ -313,4 +336,9 @@ class factory :
         :param autopilot        will generate output automatically
         :param batch (default 2k)           size of the batch
         """
+        if 'apply' not in _args :
             return Trainer(**_args)
+        elif _args['apply'] == 'shuffe' :
+            return Shuffle(**_args)
+        elif _args['apply'] == 'generate' :
+            return Generator(**_args)

data/maker/prepare/__init__.py

@@ -95,8 +95,9 @@ class Input :
             # 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')
-            _df  = self.df.nunique().T / self.df.shape[0]
-            self._io = pd.DataFrame(_df).astype(float).to_dict(orient='records')
+            _df  = pd.DataFrame(self.df.nunique().T / self.df.shape[0]).T
+            self._io =  (_df.to_dict(orient='records'))
+            
         except Exception as e:
             print (e)
             self._io = []

setup.py

@@ -4,7 +4,8 @@ import sys
 
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read() 
-args = {"name":"data-maker","version":"1.5.0","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
+args = {"name":"data-maker","version":"1.5.1",
+        "author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vumc.org","license":"MIT",
         "packages":find_packages(),"keywords":["healthcare","data","transport","protocol"]}
 args["install_requires"] = ['data-transport@git+https://dev.the-phi.com/git/steve/data-transport.git','tensorflow']
 args['url'] = 'https://hiplab.mc.vanderbilt.edu/aou/data-maker.git'