bug fix: attempt

Reviewed-on: #5

data/gan.py

@@ -103,11 +103,12 @@ class GNet :
                 CHECKPOINT_SKIPS = int(args['checkpoint_skips']) if 'checkpoint_skips' in args else int(self.MAX_EPOCHS/10)
                 
                 CHECKPOINT_SKIPS = 1 if CHECKPOINT_SKIPS < 1 else CHECKPOINT_SKIPS
+                
                 # if self.MAX_EPOCHS  < 2*CHECKPOINT_SKIPS :
                 #         CHECKPOINT_SKIPS = 2
                 # self.CHECKPOINTS = [1,self.MAX_EPOCHS] + np.repeat( np.divide(self.MAX_EPOCHS,CHECKPOINT_SKIPS),CHECKPOINT_SKIPS ).cumsum().astype(int).tolist() 
                 self.CHECKPOINTS = np.repeat(CHECKPOINT_SKIPS, self.MAX_EPOCHS/ CHECKPOINT_SKIPS).cumsum().astype(int).tolist()
-               
+                
                 self.ROW_COUNT = args['real'].shape[0] if 'real' in args else 100
                 self.CONTEXT = args['context']
                 self.ATTRIBUTES = {"id":args['column_id'] if 'column_id' in args else None,"synthetic":args['column'] if 'column' in args else None}
@@ -287,8 +288,17 @@ class Generator (GNet):
         
         """
         def __init__(self,**args):
-                GNet.__init__(self,**args)
-                self.discriminator = Discriminator(**args)
+                if 'trainer' not in args :
+                        GNet.__init__(self,**args)
+                        self.discriminator = Discriminator(**args)
+                else:
+                        _args = {}
+                        _trainer = args['trainer']
+                        for key in vars(_trainer) :
+                                value = getattr(_trainer,key)
+                                setattr(self,key,value)
+                                _args[key] = value
+                        self.discriminator = Discriminator(**_args)
         def loss(self,**args):
                 fake    = args['fake']
                 label   = args['label']
@@ -657,7 +667,9 @@ class Predict(GNet):
 
                 fake    = self.generator.network(inputs=z, label=label)
                 init    = tf.compat.v1.global_variables_initializer()
-                saver   = tf.compat.v1.train.Saver()
+                print ([self.CHECKPOINTS])
+                # saver   = tf.compat.v1.train.Saver()
+                saver = tf.compat.v1.train.Saver(max_to_keep=len(self.CHECKPOINTS))
                 df              = pd.DataFrame()
                 CANDIDATE_COUNT = args['candidates'] if 'candidates' in  args else 1 #0 if self.ROW_COUNT < 1000 else 100
                 candidates = []

data/maker/__init__.py

@@ -22,7 +22,7 @@ import nujson as json
 from multiprocessing import Process, RLock
 from datetime import datetime, timedelta
 from multiprocessing import Queue
-
+from data.maker.version import __version__
 import time
 
 
@@ -33,6 +33,7 @@ class Learner(Process):
         
         
         super(Learner, self).__init__() 
+        self._arch = {'init':_args}
         self.ndx = 0
         self._queue = Queue()
         self.lock = RLock()
@@ -44,6 +45,8 @@ class Learner(Process):
             self.gpu = None
         
         self.info = _args['info']
+        if 'context' not in self.info :
+            self.info['context'] = self.info['from']
         self.columns    = self.info['columns'] if 'columns' in self.info else None
         self.store      = _args['store']
         
@@ -97,9 +100,12 @@ class Learner(Process):
                 # __info = (pd.DataFrame(self._states)[['name','path','args']]).to_dict(orient='records')
                 if self._states :
                     __info = {}
-                    
+                    # print (self._states)
                     for key in self._states :
-                        __info[key] = [{"name":_item['name'],"args":_item['args'],"path":_item['path']} for _item in self._states[key]]
+                        _pipeline = self._states[key]
+                        
+                        # __info[key] = ([{'name':_payload['name']} for _payload in _pipeline])
+                        __info[key] = [{"name":_item['name'],"args":_item['args'],"path":_item['path']} for _item in self._states[key] if _item ]
                     self.log(object='state-space',action='load',input=__info)
         
 
@@ -173,19 +179,23 @@ class Learner(Process):
         for name in columns :
             #
             # randomly sampling 5 elements to make sense of data-types
+            
             if self._df[name].size < 5 :
                 continue
-            _index = np.random.choice(np.arange(self._df[name].size),5,False)
-            no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index]]            
+            _index = np.random.choice(np.arange(self._df[name].shape[0]),5,False)
+            no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index] if value is not None]            
             no_value = 0 if np.sum(no_value) > 0 else ''
             try:
                 self._df[name] = self._df[name].fillna(no_value)
+            except Exception as e:
+                print (['.... skipping ',name,no_value])
             finally:
                 pass
             
-            _log[name] = self._df[name].dtypes.name
-        _log = {'action':'structure','input':_log}
-        self.log(**_log)
+            # _log[name] = self._df[name].dtypes.name
+            # _log[name] = reader.meta()
+        # _log = {'action':'structure','input':_log}
+        # self.log(**_log)
         #
         # convert the data to binary here ...
         _schema = self.get_schema()       
@@ -270,18 +280,23 @@ class Trainer(Learner):
         # 
         _epochs = [_e  for _e in gTrain.logs['epochs'] if _e['path'] != '']
         _epochs.sort(key=lambda _item: _item['loss'],reverse=False)
-       
+        
         _args['network_args']['max_epochs'] = _epochs[0]['epochs']
         self.log(action='autopilot',input={'epoch':_epochs[0]})
-        g = Generator(**_args)
+        
         # g.run() 
         
         end = datetime.now() #.strftime('%Y-%m-%d %H:%M:%S')
         _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._g = g
-        if self.autopilot :            
+        
+        if self.autopilot : 
+            
+            # g = Generator(**_args)     
+            
+            g = Generator(**self._arch['init'])
+            self._g = g      
             self._g.run()   
             #
             #@TODO Find a way to have the data in the object ....
@@ -300,10 +315,15 @@ class Generator (Learner):
         #
         # We need to load the mapping information for the space we are working with ...
         #
+        
+        
         self.network_args['candidates'] = int(_args['candidates']) if 'candidates' in _args else 1
-        filename = os.sep.join([self.network_args['logs'],'output',self.network_args['context'],'map.json'])
+        # filename = os.sep.join([self.network_args['logs'],'output',self.network_args['context'],'map.json'])
+        _suffix = self.network_args['context']
+        filename = os.sep.join([self.network_args['logs'],'output',self.network_args['context'],'meta-',_suffix,'.json'])
         self.log(**{'action':'init-map','input':{'filename':filename,'exists':os.path.exists(filename)}})
         if os.path.exists(filename):
+            
             file = open(filename)
             self._map = json.loads(file.read())
             file.close()
@@ -412,66 +432,51 @@ class Generator (Learner):
             return _date.strftime(FORMAT)
 
         pass
-    def format(self,_df,_schema):
-        r = {}
+    def _format(self,_df,_schema):
+        """
+        :_df    data-frame being processed
+        :_schema    table schema with types
+        """
+        
+        
+        _columns = [_item['name'] for _item in _schema]
+        _map = {'INT64':np.int64,'FLOAT64':np.float64,'DATE':np.datetime64,'TIMESTAMP':(lambda field: pd.to_datetime(field).dt.tz_localize(None))}
+        
+        # pd.to_datetime(_df.measurement_datetime).dt.tz_localize(None)
         
         for _item in _schema :
-            name = _item['name']
-            
-            if _item['type'].upper() in ['DATE','DATETIME','TIMESTAMP'] :
-                FORMAT = '%Y-%m-%d'
-                
-                try:
+            _name = _item['name']
+            if _item['type'] not in _map :
+                continue
+            _pointer = _map[_item['type']]
+            try:
+                if type(_pointer).__name__ == 'type':
+                    if _item['type'] in ['INT64','FLOAT64'] :
+                        
+                        novalue = np.int64(0) if _item['type'] == 'INT64' else np.float64(0)
+                    elif _item['type'] == 'STRING' :
+                        novalue = ''
+                    
+                    if _item['type'] in ['INT64','FLOAT64','STRING'] :
+                        
+                        _df[_name] = _df[_name].fillna(novalue)
+                        
                     #
-                    #-- Sometimes data isn't all it's meant to be
-                    SIZE = -1
-                    if 'format' in self.info and name in self.info['format'] :
-                        FORMAT = self.info['format'][name]
-                        SIZE = 10
-                    elif _item['type'] in ['DATETIME','TIMESTAMP'] :
-                            FORMAT = '%Y-%m-%-d %H:%M:%S'
-                            SIZE = 19
-                    
-                    if SIZE > 0 :
-                        
-                        values = pd.to_datetime(_df[name], format=FORMAT).astype(np.datetime64)
-                        # _df[name] = [_date[:SIZE].strip() for _date in values]
-                        
-                       
-                    # _df[name] = _df[name].astype(str)
-                    r[name] = FORMAT
-                    # _df[name] = pd.to_datetime(_df[name], format=FORMAT) #.astype('datetime64[ns]')
-                    if _item['type'] in ['DATETIME','TIMESTAMP']:                   
-                        pass #;_df[name] = _df[name].fillna('').astype('datetime64[ns]')
-                    
-                except Exception as e:
-                    pass
-                finally:
-                    pass
-            else:
-                
-                #
-                # Because types are inferred on the basis of the sample being processed they can sometimes be wrong
-                #   To help disambiguate we add the schema information
-                _type = None
-                
-                if 'int' in _df[name].dtypes.name or 'int' in _item['type'].lower():                    
-                    _type = np.int
-                    
-                elif 'float' in _df[name].dtypes.name or 'float' in _item['type'].lower():
-                    _type = np.float
-                if _type :
-                    
-                    _df[name] = _df[name].fillna(0).replace(' ',0).replace('',0).replace('NA',0).replace('nan',0).astype(_type)
-                # else:
-                #     _df[name] = _df[name].astype(str)
-        # _df = _df.replace('NaT','').replace('NA','')
-        
-        if r :
-            self.log(**{'action':'format','input':r})
-        return _df
+                    # This is not guaranteed to work but may help ...     
+                    _df[_name] = _df[_name].values.astype(_pointer)
 
-        pass
+                    
+                    
+                else:
+                    _df[_name] = _pointer(_df[_name])
+                    pass
+            except Exception as e:
+                
+                pass
+        # bqw =  transport.factory.instance(**_store['target'])
+        # bqw.write(_df,schema=_schema)
+        
+        return _df[_columns]
     def post(self,_candidates):
         
         if 'target'  in self.store :
@@ -483,42 +488,58 @@ class Generator (Learner):
         else:
             _store = None
         N = 0
+        
+
+        _haslist = np.sum([type(_item)==list for _item in self.columns]) > 0
+        _schema = self.get_schema()
+        
         for _iodf in _candidates :
             _df = self._df.copy()
-            _df[self.columns] = _iodf[self.columns]
+            
+            if self.columns and _haslist is False:
+                _df[self.columns] = _iodf[self.columns]
+            else:
+                #
+                # In here we have the case of all attributes have undergone random permutations
+                # 
+                _df = _iodf
+            
+              
             N += _df.shape[0]
             if self._states and 'post' in self._states:
                 _df = State.apply(_df,self._states['post'])
-            # #
-            # #@TODO:
-            # # Improve formatting with better post-processing pipeline
-            # if 'approximate' in self.info :                
-            #     _df = self.approximate(_df)
-            # if 'make_date' in self.info :
-            #     for name in self.info['make_date'] :
-            #         # iname = self.info['make_date']['init_field']
-            #         iname = self.info['make_date'][name]
-
-            #         years = _df[iname]
-            #         _dates = [self.make_date(year=_year,field=name) for _year in years]
-            #         if _dates :                         
-            #             _df[name] = _dates
-                        
+                
+            #
+            # Let us format the data frame so as to be able to minimize write errors
+            #
+            if _schema :
+                _df = self._format(_df,_schema) 
+                
+                
                         
            
-            _schema = self.get_schema()
             
-            _df = self.format(_df,_schema)
-            _log = [{"name":_schema[i]['name'],"dataframe":_df[_df.columns[i]].dtypes.name,"schema":_schema[i]['type']} for i in np.arange(len(_schema)) ]
-            self.log(**{"action":"consolidate","input":_log})
+            
+            # _df = self.format(_df,_schema)
+            # _log = [{"name":_schema[i]['name'],"dataframe":_df[_df.columns[i]].dtypes.name,"schema":_schema[i]['type']} for i in np.arange(len(_schema)) ]
+            self.log(**{"action":"consolidate","input":{"rows":N,"candidate":_candidates.index(_iodf)}})
 
-           
+            
             if _store :
-                writer = transport.factory.instance(**_store)
-                if _store['provider'] == 'bigquery':
-                    writer.write(_df,schema=[],table=self.info['from'])
+                _log = {'action':'write','input':{'table':self.info['from'],'schema':[],'rows':_df.shape[0]}}
+               
+                writer = transport.factory.instance(**_store)    
+
+                if _store['provider'] == 'bigquery' and _schema:
+                    try:
+                        _log['schema'] = _schema
+                        writer.write(_df,schema=_schema)
+                    except Exception as e:
+                        print (e)
+                        writer.write(_df)
                 else:
-                    writer.write(_df,table=self.info['from'])
+                    writer.write(_df)
+                self.log(**_log)
             else:
                 self.cache.append(_df)
                 
@@ -533,30 +554,63 @@ class Shuffle(Generator):
     """
     def __init__(self,**_args):
         super().__init__(**_args)
+        if 'data' not in _args :
+            reader = transport.factory.instance(**self.store['source'])            
+            self._df = reader.read(sql=self.info['sql'])
+        
     def run(self):
         
-        np.random.seed(1)
+        
+        
         self.initalize()
-        _index = np.arange(self._df.shape[0])
-        np.random.shuffle(_index)
-        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(_index.size))
-        _iodf = pd.DataFrame(self._df[_iocolumns].copy(),index = np.arange(_index.size))
-        # self._df = self._df.loc[_index][_ocolumns].join(_iodf)
-        self._df = self._df.loc[_index][_ocolumns]
-        self._df.index = np.arange(self._df.shape[0])
-        self._df = self._df.join(_iodf)
         #
-        # The following is a full shuffle 
-        self._df = self._df.loc[_index]
-        self._df.index = np.arange(self._df.shape[0])
+        # If we are given lists of columns instead of a list-of-list
+        #   unpack the list
+        _invColumns = []
+        _colNames = []
+        _ucolNames= []
+        _rmColumns = []
+        for _item in self.info['columns'] :
+            if type(_item) == list :
+                _invColumns.append(_item)
+                _rmColumns += _item
+
+            elif _item in self._df.columns.tolist():
+                _colNames.append(_item)
+        #
+        # At this point we build the matrix of elements we are interested in considering the any unspecified column
+        #
         
+        if _colNames :
+            _invColumns.append(_colNames)
+        _ucolNames = list(set(self._df.columns) - set(_colNames) - set(_rmColumns))
+        if _ucolNames :
+            _invColumns += [ [_name] for _name in _ucolNames]
+
+        _xdf = pd.DataFrame()
+        _xdf = pd.DataFrame()
+        _index = np.arange(self._df.shape[0])
         
+        for _columns in _invColumns :
+            
+            _tmpdf = self._df[_columns].copy()[_columns]
+            np.random.seed(1)
+            np.random.shuffle(_index)
+            
+            # _values = _tmpdf.values[_index]
+            #_tmpdf = _tmpdf.iloc[_index]
+            _tmpdf = pd.DataFrame(_tmpdf.values[_index],columns=_columns)
+            if _xdf.shape[0] == 0 :
+                _xdf = _tmpdf
+            else:
+                _xdf = _xdf.join(_tmpdf)
+
+        _xdf = _xdf[self._df.columns]            
+        self._df = _xdf
         _log = {'action':'io-data','input':{'candidates':1,'rows':int(self._df.shape[0])}}
         self.log(**_log)
         try:
+            
             self.post([self._df])
             self.log(**{'action':'completed','input':{'candidates':1,'rows':int(self._df.shape[0])}})
         except Exception as e :
@@ -580,6 +634,7 @@ class factory :
         
         """
         
+        #
         
         if _args['apply'] in [apply.RANDOM] :
             pthread =  Shuffle(**_args)

data/maker/apply.py

@@ -69,7 +69,7 @@ class Date(Post):
         """
         
         """
-      pass
+        pass
 class Approximate(Post):
     def apply(**_args):
         pass

data/maker/state/__init__.py

@@ -31,12 +31,22 @@ class State :
                 continue
             
             pointer = _item['module']
-            _args = _item['args']
+            
+            if type(pointer).__name__ != 'function':
+                _args = _item['args'] if 'args' in _item else {}
+            else:
+                pointer   = _item['module']
+            
+            _args = _item['args'] if 'args' in _item else {}
+
             
             _data = pointer(_data,_args)
         return _data
     @staticmethod
     def instance(_args):
+        """
+        
+        """
         pre = []
         post=[]
        
@@ -45,8 +55,20 @@ class State :
             #
             # If the item has a path property is should be ignored
             path  = _args[key]['path'] if 'path' in _args[key] else ''
-            out[key] = [ State._build(dict(_item,**{'path':path})) if 'path' not in _item else State._build(_item) for _item in _args[key]['pipeline']]
-            
+            # out[key] = [ State._build(dict(_item,**{'path':path})) if 'path' not in _item else State._build(_item) for _item in _args[key]['pipeline']]
+            out[key]  = []
+            for _item in _args[key]['pipeline'] :
+                
+                if type(_item).__name__ == 'function':
+                    _stageInfo = {'module':_item,'name':_item.__name__,'args':{},'path':''}
+                    pass
+                else:
+                    if 'path' in _item :
+                        _stageInfo = State._build(dict(_item,**{'path':path}))
+                    else :
+                        _stageInfo= State._build(_item)
+                out[key].append(_stageInfo)
+        # print ([out])
         return out
         # if 'pre' in _args:
         #     path  = _args['pre']['path'] if 'path' in _args['pre'] else ''
@@ -68,11 +90,18 @@ class State :
         pass
     @staticmethod
     def _build(_args):
-        
+        """
+        This function builds the object {module,path} where module is extracted from a file (if needed)
+        :param _args   dictionary containing attributes that can be value pair
+                       It can also be a function
+        """
+        #
+        # In the advent an actual pointer is passed we should do the following
+    
         _info = State._extract(_args)
         # _info = dict(_args,**_info)
         
-        _info['module'] = State._instance(_info)
+        _info['module'] = State._instance(_info) 
         return _info if _info['module'] is not None else None
             
     @staticmethod

data/maker/version.py

@@ -0,0 +1 @@
+__version__='1.7.6'

setup.py

@@ -1,10 +1,10 @@
 from setuptools import setup, find_packages
 import os
 import sys
-
+import version
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read() 
-args = {"name":"data-maker","version":"1.6.4",
+args = {"name":"data-maker","version":version.__version__,
         "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://github.com/lnyemba/data-transport.git','tensorflow']

version.py

@@ -0,0 +1 @@
+data/maker/version.py