bug fix & enhancements

data/gan.py

@@ -533,7 +533,7 @@ class Train (GNet):
                                         print(format_str % (epoch, -w_sum/(self.STEPS_PER_EPOCH*2), duration))
                                         # print (dir (w_distance))
 
-                                        logs.append({"epoch":epoch,"distance":-w_sum/(self.STEPS_PER_EPOCH*2) })
+                                        logs.append({"epoch": int(epoch),"distance":float(-w_sum/(self.STEPS_PER_EPOCH*2)) })
 
                                         # if epoch % self.MAX_EPOCHS == 0:
                                         if epoch in [5,10,20,50,75, self.MAX_EPOCHS] :
@@ -547,6 +547,7 @@ class Train (GNet):
                                                 if self.logger :
                                                         row = {"module":"gan-train","action":"logs","input":{"partition":self.PARTITION,"logs":logs}} #,"model":pickle.dump(sess)}                                                        
                                                         self.logger.write(row)
+                                                        
                                                         #
                                                         # @TODO:
                                                         # We should upload the files in the checkpoint 

data/maker/__init__.py

@@ -69,15 +69,19 @@ class Learner(Process):
         self.cache = []
         # self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
         # sel.max_epoc
+        self.logger = None
+        if 'logger' in self.store :
+            self.logger = transport.factory.instance(**self.store['logger'])
     def log(self,**_args):
         
         try:
-            # _context = self.info['context']
+            _context = self.info['context']
-            # _label = self.info['info'] if 'info' in self.info else _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})
+            _args = dict({'ndx':self.ndx,'module':self.name,'table':self.info['from'],'context':_context,'info':_label,**_args})
-            # logger.write(_args)
+            if self.logger:
-            # self.ndx += 1
+                self.logger.write(_args)
+            self.ndx += 1
             # if hasattr(logger,'close') :
             #     logger.close()
             pass
@@ -178,6 +182,8 @@ class Trainer(Learner):
             _args['gpu'] = self.gpu
         _args['real'] = _matrix
         _args['candidates'] = self.candidates
+        if self.logger :
+            _args['logger'] = transport.factory.instance(**self.store['logger'])
         #
         # At this point we have the binary matrix, we can initiate training
         #
@@ -250,6 +256,8 @@ class Generator (Learner):
         _args['row_count'] = self._df.shape[0]
         if self.gpu :
             _args['gpu'] = self.gpu
+        if self.logger :
+            _args['logger'] = transport.factory.instance(**self.store['logger'])        
         gHandler = gan.Predict(**_args)
         gHandler.load_meta(columns=None)
         _iomatrix = gHandler.apply()        

data/maker/prepare/__init__.py

@@ -34,6 +34,8 @@ class Hardware :
     pass
 
 class Input :
+    class NOVALUES :
+        RANDOM,IGNORE,ALWAYS = ['random','ignore','always']
     """
     This class is designed to read data from a source and and perform a variet of operations :
         - provide a feature space, and rows (matrix profile)
@@ -257,8 +259,6 @@ class Input :
     def decode (self,_matrix,**_args):
         #
         # _matrix   binary matrix
-        # _values   value space given the columns
-        # columns   name of the columns ...
         #
       
         columns = _args['columns']
@@ -268,8 +268,15 @@ class Input :
         #@TODO: Provide random values for things that are missing
         
         # x = _matrix.apply(lambda row: _values[row.values == 1].tolist()[0] if (row.values == 1).sum() > 0 else np.repeat(None,len(self._columns)) ,axis=1).tolist()
-        novalues = _values[np.random.choice( len(_values),1)[0]].tolist()
+        #
-        # novalues = np.repeat(None,len(self._columns))
+        # @TODO: Provide a parameter to either:
+        #   - missing = {outlier,random,none}
+        #   -   outlier: select an outlier, random: randomly select a value, none: do nothing ...
+        # 
+        if np.random.choice([0,1],1)[0] :
+            novalues = _values[np.random.choice( len(_values),1)[0]].tolist()
+        else:
+            novalues = np.repeat(None,len(self._columns))
         x = _matrix.apply(lambda row: _values[row.values == 1].tolist()[0] if (row.values == 1).sum() > 0 else novalues ,axis=1).tolist()
         return pd.DataFrame(x,columns=columns)