fixes with the framework - only supports single feature

README.md

@@ -15,22 +15,29 @@ After installing the easiest way to get started is as follows (using pandas). Th
 1. Train the GAN on the original/raw dataset
 
 
-        import pandas as pd
+import pandas as pd
-        import data.maker
+import data.maker
 
-        df  = pd.read_csv('myfile.csv')
+df      = pd.read_csv('sample.csv')
-        cols= ['f1','f2','f2']  
+column  = 'gender'
-        data.maker.train(data=df,cols=cols,logs='logs')
+id      = 'id' 
+context = 'demo'
+data.maker.train(context=context,data=df,column=column,id=id,logs='logs')
+
+The trainer will store the data on disk (for now) in a structured folder that will hold training models that will be used to generate the synthetic data.
 
-2. Generate a candidate dataset from the learnt features
 
+2. Generate a candidate dataset from the learnt features
 
-    import pandas as pd
-    import data.maker
 
-    df = data.maker.generate(logs='logs')
+import pandas as pd
-    df.head()
+import data.maker
 
+df  = pd.read_csv('sample.csv')
+id  = 'id'
+column = 'gender'
+context = 'demo'
+data.maker.generate(data=df,id=id,column=column,logs='logs')
 
 ## Limitations
 

data/__init__.py

@@ -0,0 +1 @@
+import data.params as params

data/gan.py

@@ -11,8 +11,8 @@ import pandas as pd
 import time
 import os
 import sys
-from params import SYS_ARGS
+from data.params import SYS_ARGS
-from bridge import Binary
+from data.bridge import Binary
 import json
 
 os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
@@ -37,8 +37,6 @@ class GNet :
         self.layers = void()
         self.layers.normalize = self.normalize
 
-        self.get = void()
-        self.get.variables = self._variable_on_cpu
 
         self.NUM_GPUS = 1
        
@@ -64,6 +62,10 @@ class GNet :
         self._REAL = args['real'] if 'real' in args else None
         self._LABEL = args['label'] if 'label' in args else None
 
+        self.get = void()
+        self.get.variables = self._variable_on_cpu
+
+        self.get.suffix = lambda : "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
         self.init_logs(**args)
 
     def init_logs(self,**args):
@@ -83,7 +85,9 @@ class GNet :
         This function is designed to accomodate the uses of the sub-classes outside of a strict dependency model.
         Because prediction and training can happen independently
         """
-        _name = os.sep.join([self.out_dir,'meta-'+column+'.json'])
+        # suffix = "-".join(column) if isinstance(column,list)else column
+        suffix = self.get.suffix()
+        _name = os.sep.join([self.out_dir,'meta-'+suffix+'.json'])
         if os.path.exists(_name) :
             attr = json.loads((open(_name)).read())
             for key in attr :
@@ -111,7 +115,10 @@ class GNet :
             key = args['key']
             value= args['value']
             object[key] = value
-        _name = os.sep.join([self.out_dir,'meta-'+SYS_ARGS['column']])
+        # suffix = "-".join(self.column) if isinstance(self.column,list) else self.column
+        suffix = self.get.suffix()
+        _name = os.sep.join([self.out_dir,'meta-'+suffix])
+        
         f = open(_name+'.json','w')
         f.write(json.dumps(object))
     def mkdir (self,path):
@@ -285,7 +292,9 @@ class Train (GNet):
         self.discriminator = Discriminator(**args)
         self._REAL = args['real']
         self._LABEL= args['label']
+        self.column = args['column']
         # print ([" *** ",self.BATCHSIZE_PER_GPU])
+        
         self.log_meta()
     def load_meta(self, column):
         """
@@ -407,8 +416,9 @@ class Train (GNet):
                     format_str = 'epoch: %d, w_distance = %f (%.1f)'
                     print(format_str % (epoch, -w_sum/(self.STEPS_PER_EPOCH*2), duration))
                     if epoch % self.MAX_EPOCHS == 0:
-                        
+                        # suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
-                        _name  = os.sep.join([self.train_dir,self.ATTRIBUTES['synthetic']])
+                        suffix = self.get.suffix()
+                        _name  = os.sep.join([self.train_dir,suffix])
                         # saver.save(sess, self.train_dir, write_meta_graph=False, global_step=epoch)
                         saver.save(sess, _name, write_meta_graph=False, global_step=epoch)
                         #
@@ -421,13 +431,15 @@ class Predict(GNet):
     def __init__(self,**args):
         GNet.__init__(self,**args)        
         self.generator = Generator(**args)        
-        self.values  = values
+        self.values  = args['values']
     def load_meta(self, column):
         super().load_meta(column)
         self.generator.load_meta(column)
     def apply(self,**args):
         # print (self.train_dir)
-        model_dir = os.sep.join([self.train_dir,self.ATTRIBUTES['synthetic']+'-'+str(self.MAX_EPOCHS)])
+        # suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
+        suffix = self.get.suffix()
+        model_dir = os.sep.join([self.train_dir,suffix+'-'+str(self.MAX_EPOCHS)])
         demo = self._LABEL #np.zeros([self.ROW_COUNT,self.NUM_LABELS]) #args['de"shape":{"LABEL":list(self._LABEL.shape)} mo']
         tf.compat.v1.reset_default_graph()
         z = tf.random.normal(shape=[self.BATCHSIZE_PER_GPU, self.Z_DIM])
@@ -450,19 +462,24 @@ class Predict(GNet):
             # if we are dealing with numeric values only we can perform a simple marginal sum against the indexes
             #
 
-            df =  ( pd.DataFrame(np.round(f).astype(np.int32),columns=values))
+            df =  ( pd.DataFrame(np.round(f).astype(np.int32)))
             # i = df.T.index.astype(np.int32) #-- These are numeric pseudonyms
             # df = (i * df).sum(axis=1)
             #
             # In case we are dealing with actual values like diagnosis codes we can perform 
             #
-            r = np.zeros((self.ROW_COUNT,1))
+            columns = self.ATTRIBUTES['synthetic'] if isinstance(self.ATTRIBUTES['synthetic'],list)else [self.ATTRIBUTES['synthetic']]
+            
+            r = np.zeros((self.ROW_COUNT,len(columns)))
             for col in df :
                 i = np.where(df[col])[0]
                 r[i] = col
-            df = pd.DataFrame(r,columns=[self.ATTRIBUTES['synthetic']])
             
-            return df.to_dict(orient='list')
+            df = pd.DataFrame(r,columns=columns)
+            
+            df[df.columns] = (df.apply(lambda value: self.values[ int(value)],axis=1))
+            return df.to_dict(orient='lists')
+            # return df.to_dict(orient='list')
             # count = str(len(os.listdir(self.out_dir)))
             # _name = os.sep.join([self.out_dir,self.CONTEXT+'-'+count+'.csv'])
             # df.to_csv(_name,index=False)
@@ -476,7 +493,7 @@ class Predict(GNet):
             #         idx2 = (demo[:, n] == 1)
             #         idx = [idx1[j] and idx2[j] for j in range(len(idx1))]
             #         num = np.sum(idx)
-            #         print ("_____________________")
+            #         print ("___________________list__")
             #         print (idx1)
             #         print (idx2)
             #         print (idx)
@@ -531,7 +548,8 @@ if __name__ == '__main__' :
     elif 'generate' in SYS_ARGS:
         values = df[column].unique().tolist()
         values.sort()
-        p = Predict(context=context,label=LABEL,values=values)
+        
+        p = Predict(context=context,label=LABEL,values=values,column=column)
         p.load_meta(column)
         r = p.apply()
         print (df)
@@ -539,6 +557,7 @@ if __name__ == '__main__' :
         df[column] = r[column]
         print (df)
         
+        
     else:
         print (SYS_ARGS.keys())
         print (__doc__)

data/maker/__init__.py

@@ -0,0 +1,68 @@
+"""
+(c) 2019 Data Maker, hiplab.mc.vanderbilt.edu
+version 1.0.0
+
+This package serves as a proxy to the overall usage of the framework.
+This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques
+
+@TODO:
+    - Make configurable GPU, EPOCHS
+"""
+import pandas as pd
+import numpy as np
+from data import gan
+
+def train (**args) :
+    """
+    This function is intended to train the GAN in order to learn about the distribution of the features
+    :column     columns that need to be synthesized (discrete)
+    :logs       where the output of the (location on disk)
+    :id         identifier of the dataset
+    :data       data-frame to be synthesized
+    :context    label of what we are synthesizing
+    """
+    column = args['column']
+    
+    column_id  = args['id']
+    df  = args['data']
+    logs    = args['logs']
+    real    = pd.get_dummies(df[column]).astype(np.float32).values
+    
+    labels  = pd.get_dummies(df[column_id]).astype(np.float32).values
+    max_epochs = 10
+    context = args['context']
+    trainer = gan.Train(context=context,max_epochs=max_epochs,real=real,label=labels,column=column,column_id=column_id)
+    return trainer.apply()
+
+def generate(**args):
+    """
+    This function will generate a synthetic dataset on the basis of a model that has been learnt for the dataset
+    @return pandas.DataFrame
+
+    :data   data-frame to be synthesized
+    :column   columns that need to be synthesized (discrete)
+    :id     column identifying an entity
+    :logs   location on disk where the learnt knowledge of the dataset is
+    """
+    df      = args['data']
+    
+    column      = args['column'] 
+    column_id   = args['id']
+    logs        = args['logs']
+    context = args['context']
+    #
+    #@TODO:
+    #   If the identifier is not present, we should fine a way to determine or make one
+    #
+    #ocolumns= list(set(df.columns.tolist())- set(columns))
+    
+    values = df[column].unique().tolist()
+    values.sort()
+
+    labels = pd.get_dummies(df[column_id]).astype(np.float32).values
+    handler = gan.Predict (context=context,label=labels,values=values,column=column)
+    handler.load_meta(column)
+    r =  handler.apply()
+    _df = df.copy()
+    _df[column] = r[column]
+    return _df

setup.py

@@ -5,7 +5,7 @@ import sys
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read() 
 args = {"name":"data-maker","version":"1.0.0","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
-        "packages":["data-maker"],"keywords":["healthcare","edi","x12","data","transport","protocol"]}
+        "packages":["data-maker"],"keywords":["healthcare","data","transport","protocol"]}
 args["install_requires"] = ['data-transport@git+https://dev.the-phi.com/git/steve/data-transport.git','numpy','pandas','pandas-gbq','pymongo']
 args['url'] = 'https://hiplab.mc.vanderbilt.edu/aou/gan.git'
 if sys.version_info[0] == 2 :