bug fixes

data/gan.py

@@ -1,8 +1,23 @@
 """
-usage :
+This code was originally writen by Ziqi Zhang <ziqi.zhang@vanderbilt.edu> in order to generate synthetic data.
-    optional :
+The code is an implementation of a Generative Adversarial Network that uses the Wasserstein Distance (WGAN).
-    --num_gpu   number of gpus to use will default to 1
+It is intended to be used in 2 modes (embedded in code or using CLI)
-    --epoch     steps per epoch default to 256
+
+USAGE :
+
+The following parameters should be provided in a configuration file (JSON format)
+python data/maker --config <path-to-config-file.json>
+
+CONFIGURATION FILE STRUCTURE :
+
+    context     what it is you are loading (stroke, hypertension, ...)
+    data        path of the file to be loaded
+    logs        folder to store training model and meta data about learning
+    max_epochs  number of iterations in learning 
+    num_gpu     number of gpus to be used (will still run if the GPUs are not available)
+
+EMBEDDED IN CODE :
+
 """
 import tensorflow as tf
 from tensorflow.contrib.layers import l2_regularizer
@@ -426,7 +441,7 @@ 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":epoch,"distance":-w_sum })
 
                     if epoch % self.MAX_EPOCHS == 0:
                         # suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']

data/maker/__init__.py

@@ -24,21 +24,25 @@ def train (**args) :
     column      = args['column']
     
     column_id   = args['id']
-    df          = args['data']
+    df          = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
-    logs        = args['logs']
+    # logs        = args['logs']
-    real        = pd.get_dummies(df[column]).astype(np.float32).values
+    # real        = pd.get_dummies(df[column]).astype(np.float32).values
-    labels      = pd.get_dummies(df[column_id]).astype(np.float32).values
+    # labels      = pd.get_dummies(df[column_id]).astype(np.float32).values
-    num_gpu     = 1 if 'num_gpu' not in args else args['num_gpu']
+    args['real']        = pd.get_dummies(df[column]).astype(np.float32).values
-    max_epochs  = 10 if 'max_epochs' not in args else args['max_epochs']
+    args['label']      = pd.get_dummies(df[column_id]).astype(np.float32).values
+    # num_gpu     = 1 if 'num_gpu' not in args else args['num_gpu']
+    # max_epochs  = 10 if 'max_epochs' not in args else args['max_epochs']
     context     = args['context']
+
     if 'store' in args :
         args['store']['args']['doc'] = context
         logger = factory.instance(**args['store'])
+        args['logger'] = logger
         
     else:
         logger = None
-        
+    trainer = gan.Train(**args)    
-    trainer     = gan.Train(context=context,max_epochs=max_epochs,num_gpu=num_gpu,real=real,label=labels,column=column,column_id=column_id,logger = logger,logs=logs)
+    # trainer     = gan.Train(context=context,max_epochs=max_epochs,num_gpu=num_gpu,real=real,label=labels,column=column,column_id=column_id,logger = logger,logs=logs)
     return trainer.apply()
 
 def generate(**args):
@@ -51,14 +55,14 @@ def generate(**args):
     :id     column identifying an entity
     :logs   location on disk where the learnt knowledge of the dataset is
     """
-    df      = args['data']
+    # df      = args['data']
-    
+    df      = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
     column      = args['column'] 
     column_id   = args['id']
-    logs        = args['logs']
+    # logs        = args['logs']
-    context = args['context']
+    # context = args['context']
-    num_gpu     = 1 if 'num_gpu' not in args else args['num_gpu']
+    # num_gpu     = 1 if 'num_gpu' not in args else args['num_gpu']
-    max_epochs  = 10 if 'max_epochs' not in args else args['max_epochs']
+    # max_epochs  = 10 if 'max_epochs' not in args else args['max_epochs']
 
     #
     #@TODO:
@@ -69,8 +73,11 @@ def generate(**args):
     values = df[column].unique().tolist()
     values.sort()
 
-    labels = pd.get_dummies(df[column_id]).astype(np.float32).values
+    # labels = pd.get_dummies(df[column_id]).astype(np.float32).values
-    handler = gan.Predict (context=context,label=labels,max_epochs=max_epochs,num_gpu=num_gpu,values=values,column=column,logs=logs)
+    args['label']  = pd.get_dummies(df[column_id]).astype(np.float32).values
+    args['values']  = values
+    # handler = gan.Predict (context=context,label=labels,max_epochs=max_epochs,num_gpu=num_gpu,values=values,column=column,logs=logs)
+    handler = gan.Predict (**args)
     handler.load_meta(column)
     r =  handler.apply()
     _df = df.copy()

data/maker/__main__.py

@@ -1,10 +1,27 @@
 import pandas as pd
 import data.maker
-
+from data.params import SYS_ARGS
-df      = pd.read_csv('sample.csv')
+import json
-column  = 'gender'
+from scipy.stats import wasserstein_distance as wd
-id      = 'id' 
+import risk
-context = 'demo'
+import numpy as np
-store = {"type":"mongo.MongoWriter","args":{"host":"localhost:27017","dbname":"GAN"}}
+if 'config' in SYS_ARGS :
-max_epochs = 11
+    ARGS = json.loads(open(SYS_ARGS['config']).read())
-data.maker.train(store=store,max_epochs=max_epochs,context=context,data=df,column=column,id=id,logs='foo')
+    if 'generate' not in SYS_ARGS :
+        data.maker.train(**ARGS)    
+    else:
+        #
+        #
+        _df = data.maker.generate(**ARGS)
+        odf = pd.read_csv (ARGS['data'])
+        odf.columns = [name.lower() for name in odf.columns]
+        column = [ARGS['column'] ] #+ ARGS['id']
+        print (column)
+        print (_df[column].risk.evaluate())
+        print (odf[column].risk.evaluate())
+        _x = pd.get_dummies(_df[column]).values
+        y  = pd.get_dummies(odf[column]).values
+        N = _df.shape[0]
+        print (np.mean([ wd(_x[i],y[i])for i in range(0,N)]))
+        # column = SYS_ARGS['column']
+        # odf = open(SYS_ARGS['data'])