bug fix: encoding/decoding to improve correlations between attributes

data/maker/__init__.py

@@ -13,13 +13,17 @@ import numpy as np
 import data.gan as gan
 import transport
 # from data.bridge import Binary
-import threading as thread
+import threading
 from data.maker import prepare
 import copy
 import os
-import json
+import nujson as json
 from multiprocessing import Process, RLock
 from datetime import datetime, timedelta
+from multiprocessing import Queue
+
+import time
+
 
 class Learner(Process):
 
@@ -28,6 +32,7 @@ class Learner(Process):
         
         super(Learner, self).__init__() 
         self.ndx = 0
+        self._queue = Queue()
         self.lock = RLock()
         if 'gpu' in _args :
             
@@ -61,34 +66,38 @@ class Learner(Process):
         
         _log = {'action':'init','gpu':(self.gpu if self.gpu is not None else -1)}
         self.log(**_log)
-
+        self.cache = []
         # self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
         # sel.max_epoc
     def log(self,**_args):
-        # self.lock.acquire()
+        
         try:
-            _context = self.info['context']
-            _label = self.info['info'] if 'info' in self.info else _context
-            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'],'context':_context,'info':_label,**_args})
-            logger.write(_args)
-            self.ndx += 1
-            if hasattr(logger,'close') :
-                logger.close()
+            # _context = self.info['context']
+            # _label = self.info['info'] if 'info' in self.info else _context
+            # logger = transport.factory.instance(**self.store['logger']) if 'logger' in self.store else transport.factory.instance(provider=transport.providers.CONSOLE,context='write',lock=True)
+            # _args = dict({'ndx':self.ndx,'module':self.name,'table':self.info['from'],'context':_context,'info':_label,**_args})
+            # logger.write(_args)
+            # self.ndx += 1
+            # if hasattr(logger,'close') :
+            #     logger.close()
+            pass
         except Exception as e:
             print ()
             print (_args)
             print (e)
             pass
         finally:
-            # self.lock.release()
+           
             pass
     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])]
-        else:
+        # 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])]
+        # else:
+        #     reader  = transport.factory.instance(**self.store['source'])
+        #     return reader.meta(table=self.info['from'])
         reader  = transport.factory.instance(**self.store['source'])
         return reader.meta(table=self.info['from'])
+
     def initalize(self):
         reader  = transport.factory.instance(**self.store['source'])
         _read_args= self.info
@@ -124,6 +133,25 @@ class Learner(Process):
         self._encoder = prepare.Input(**_args)  if self._df.shape[0] > 0 else None             
         _log = {'action':'data-prep','input':{'rows':int(self._df.shape[0]),'cols':int(self._df.shape[1]) } }
         self.log(**_log)
+    def get(self):
+        
+        if self.cache :
+            return self.cache if len(self.cache) > 0 else(self.cache if not self.cache else self.cache[0])
+        else:
+            return self._queue.get() if self._queue.qsize() >  0 else []
+         
+    def listen(self):
+        while True :
+            _info = self._queue.get()
+            self.cache.append(_info)
+            self._queue.task_done()
+    def publish(self,caller):
+        if hasattr(caller,'_queue') :           
+            _queue = caller._queue
+            _queue.put(self.cache)
+            
+            # _queue.join()
+        pass
 class Trainer(Learner):
     """
     This will perform training using a GAN
@@ -157,7 +185,8 @@ class Trainer(Learner):
         gTrain = gan.Train(**_args)
         gTrain.apply()
        
-        writer = transport.factory.instance(provider='file',context='write',path=os.sep.join([gTrain.out_dir,'map.json']))
+        writer = transport.factory.instance(provider=transport.providers.FILE,context='write',path=os.sep.join([gTrain.out_dir,'map.json']))
+        
         writer.write(self._encoder._map,overwrite=True)
         writer.close()
 
@@ -174,9 +203,14 @@ class Trainer(Learner):
         _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
+        self._g = g
         if self.autopilot :            
-            self.generate.run()
+            self._g.run()   
+            #
+            #@TODO Find a way to have the data in the object ....
+
+   
+   
     def generate (self):
         if self.autopilot :
             print( "Autopilot is set ... No need to call this function")
@@ -224,6 +258,7 @@ class Generator (Learner):
         _size = np.sum([len(_item) for _item in _iomatrix])
         _log = {'action':'io-data','input':{'candidates':len(_candidates),'rows':int(_size)}}
         self.log(**_log)
+        # self.cache = _candidates
         self.post(_candidates)
     def approximate(self,_df):
         _columns = self.info['approximate']
@@ -359,12 +394,14 @@ class Generator (Learner):
         pass
     def post(self,_candidates):
         
+        if 'target'  in self.store :
             _store = self.store['target'] if 'target' in self.store else {'provider':'console'}
             _store['lock'] = True
             _store['context'] = 'write' #-- Just in case
             if 'table' not in _store :
                 _store['table'] = self.info['from']
-        
+        else:
+            _store = None
         N = 0
         for _iodf in _candidates :
             _df = self._df.copy()
@@ -397,12 +434,14 @@ class Generator (Learner):
             # w.write(_df)
             # cols = [name for name in _df.columns if name.endswith('datetime')]
             # print (_df[cols])
-            
+            if _store :
                 writer = transport.factory.instance(**_store)
                 if _store['provider'] == 'bigquery':
                     writer.write(_df,schema=[],table=self.info['from'])
                 else:
                     writer.write(_df,table=self.info['from'])
+            else:
+                self.cache.append(_df)
                 
             
 
@@ -444,6 +483,8 @@ class Shuffle(Generator):
         except Exception as e :
             # print (e)
             self.log(**{'action':'failed','input':{'msg':e,'info':self.info}})
+class apply :
+    TRAIN,GENERATE,RANDOM = 'train','generate','random'
 class factory :
     _infocache = {}
     @staticmethod
@@ -459,10 +500,10 @@ class factory :
         :param batch (default 2k)           size of the batch
         """
         
-        if _args['apply'] == 'shuffle' :
-            return Shuffle(**_args)
-        elif _args['apply'] == 'generate' :
-            return Generator(**_args)
+        if _args['apply'] in [apply.RANDOM] :
+            pthread =  Shuffle(**_args)
+        elif _args['apply'] == apply.GENERATE :
+            pthread =  Generator(**_args)
         else:
             pthread= Trainer(**_args)
         if 'start' in _args and _args['start'] == True :

data/maker/prepare/__init__.py

@@ -47,6 +47,15 @@ class Input :
         :param sql      sql query  that pulls a representative sample of the data
         """
         self._schema = _args['schema'] if 'schema' in _args else {}
+        #
+        # schema data should be in a hash map for these purposes
+        #
+        if self._schema :
+            r = {}
+            for _item in self._schema :
+                r[_item['name']] = r[_item['type']]
+                self._schema = r
+        
         self.df = _args['data']
         if 'sql' not in _args :
             self._initdata(**_args)   
@@ -60,6 +69,7 @@ class Input :
         #
         self._map = {} if 'map' not in _args else _args['map']
         
+        
     def _initsql(self,**_args):
         """
         This function will initialize the class on the basis of a data-store and optionally pre-defined columns to be used to be synthesized 
@@ -73,6 +83,10 @@ class Input :
             self._initcols(data=self.df,columns=_args['columns'])
         
         pass     
+    def _init_map(self,values):
+        self._map = dict(zip(np.arange(len(values)),values))
+        for key in self._map :
+            self._map[key] = self._map[key].tolist()
     def _initcols (self,**_args) :
         """
         This function will initialize the columns to be synthesized and/or determine which ones can be synthesized
@@ -109,7 +123,7 @@ class Input :
         """
         self._initcols(**_args)
 
-    def convert(self,**_args):
+    def _convert(self,**_args):
         """
         This function will convert a data-frame into a binary matrix and provide a map to be able to map the values back to the matrix 
         :param columns  in case we specify the columns to account for (just in case the original assumptions don't hold)
@@ -150,7 +164,7 @@ class Input :
         
         return _values,_m
         
-    def revert(self,**_args) :
+    def _revert(self,**_args) :
         """
         This function will take in a binary matrix and based on the map of values it will repopulate it with values
         :param _matrix  binary matrix
@@ -186,7 +200,9 @@ class Input :
             # r[key] = [columns[np.where(row == 1) [0][0] ] for row in _matrix[:,_beg:_end]]
             
             r[key] = [columns[np.where(row==1)[0][0]] if np.where(row==1)[0].size > 0 else '' for row in _matrix]
-            
+        #
+        # we should consider decoding the matrix if possible
+        #
             
         return pd.DataFrame(r)
      
@@ -217,4 +233,39 @@ class Input :
 
         
         return cols,_matrix
+    def convert(self,**_args):
+        if 'columns' in _args or 'column' in _args :
+            columns = _args['columns'] if 'columns' in _args else [_args['column']]
+        else:
+            columns = self._columns
+        _df = self.df if 'data' not in _args else _args['data']
+        _values,_matrix = self.encode(_df,columns)
+        _, _matrix =  self.tobinary(_matrix)
+        self._init_map(_values)
+        return _values,_matrix  #-- matrix has been updated !
+    def revert(self,**_args):
+        # _columns = _args['column'] if 'column' in _args else None
+        _matrix = _args['matrix']
+        # print (_matrix)
+        return self.decode(_matrix,columns=self._columns)
+        pass
+    def encode(self,df,columns) :
+        _df = df[columns].drop_duplicates()
+        _values = _df.values.tolist()
+        _encoded = df[columns].apply(lambda row: _values.index( list(row)) ,axis=1)
+        return np.array(_values),_encoded
+    def decode (self,_matrix,**_args):
+        #
+        # _matrix   binary matrix
+        # _values   value space given the columns
+        # columns   name of the columns ...
+        #
+      
+        columns = _args['columns']
+        _values = np.array( list(self._map.values()))
+        _matrix = pd.DataFrame(_matrix) #if type(_matrix) != pd.DataFrame else _matrix
+        x = _matrix.apply(lambda row: _values[row.values == 1 ].tolist()[0] if row.values.sum() > 0 else np.repeat(None,row.size), axis=1).tolist()
+        return pd.DataFrame(x,columns=columns)
+
+