bug fix with class hierarchy

data/gan.py

@@ -76,13 +76,13 @@ class GNet :
                 # self.Z_DIM = 128 #self.X_SPACE_SIZE     
                 self.Z_DIM = 128  #-- used as rows down stream
                 self.G_STRUCTURE = [self.Z_DIM,self.Z_DIM]
+                PROPOSED_BATCH_PER_GPU = 2000 if 'batch_size' not in args else int(args['batch_size'])
+                self.BATCHSIZE_PER_GPU = PROPOSED_BATCH_PER_GPU
                 if 'real' in args : 
                         self.D_STRUCTURE = [args['real'].shape[1],256,self.Z_DIM]
-                PROPOSED_BATCH_PER_GPU = 2000 if 'batch_size' not in args else int(args['batch_size'])
+                
                         if args['real'].shape[0]  < PROPOSED_BATCH_PER_GPU :
                                 self.BATCHSIZE_PER_GPU = int(args['real'].shape[0]* 1) 
-                else:
-                        self.BATCHSIZE_PER_GPU = PROPOSED_BATCH_PER_GPU
                 # self.BATCHSIZE_PER_GPU = 2000 if 'batch_size' not in args else int(args['batch_size'])
                 self.TOTAL_BATCHSIZE = self.BATCHSIZE_PER_GPU * self.NUM_GPUS
                 self.STEPS_PER_EPOCH = 256 #int(np.load('ICD9/train.npy').shape[0] / 2000)       

data/maker/__init__.py

@@ -38,8 +38,8 @@ def train (**args) :
     # args['label']   = handler.Export(df[[column_id]])
     # args['label'] = np.ones(df.shape[0]).reshape(df.shape[0],1)
     for col in column :    
-        # args['real']        = pd.get_dummies(df[col]).astype(np.float32).values 
+        args['real']        = pd.get_dummies(df[col]).astype(np.float32).values 
-        args['real']    = handler.Export(df[[col]])
+        # args['real']    = handler.Export(df[[col]])
         args['column']  = col
         args['context'] = col
         context     = args['context']

setup.py

@@ -4,7 +4,7 @@ import sys
 
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read() 
-args = {"name":"data-maker","version":"1.1.1","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
+args = {"name":"data-maker","version":"1.1.2","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","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==1.15','pandas','pandas-gbq','pymongo']
 args['url'] = 'https://hiplab.mc.vanderbilt.edu/git/aou/data-maker.git'