@ -59,20 +59,27 @@ class GNet :
self . logs = { }
self . NUM_GPUS = 1 if ' num_gpu ' not in args else args [ ' num_gpu ' ]
if self . NUM_GPUS > 1 :
os . environ [ ' CUDA_VISIBLE_DEVICES ' ] = " 4 "
self . X_SPACE_SIZE = args [ ' real ' ] . shape [ 1 ] if ' real ' in args else 854
self . G_STRUCTURE = [ 128 , 128 ] #[self.X_SPACE_SIZE, self.X_SPACE_SIZE]
self . D_STRUCTURE = [ self . X_SPACE_SIZE , 256 , 128 ] #[self.X_SPACE_SIZE, self.X_SPACE_SIZE*2, self.X_SPACE_SIZE] #-- change 854 to number of diagnosis
# self.NUM_LABELS = 8 if 'label' not in args elif len(args['label'].shape) args['label'].shape[1]
if ' label ' in args and len ( args [ ' label ' ] . shape ) == 2 :
self . NUM_LABELS = args [ ' label ' ] . shape [ 1 ]
elif ' label ' in args and len ( args [ ' label ' ] ) == 1 :
self . NUM_LABELS = args [ ' label ' ] . shape [ 0 ]
else :
self . NUM_LABELS = 8
self . Z_DIM = 128 #self.X_SPACE_SIZE
self . BATCHSIZE_PER_GPU = args [ ' real ' ] . shape [ 0 ] if ' real ' in args else 256
# 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 ]
if ' real ' in args :
self . D_STRUCTURE = [ args [ ' real ' ] . shape [ 1 ] , 256 , self . Z_DIM ]
self . BATCHSIZE_PER_GPU = int ( args [ ' real ' ] . shape [ 0 ] * 1 ) if ' real ' in args else 256
self . TOTAL_BATCHSIZE = self . BATCHSIZE_PER_GPU * self . NUM_GPUS
self . STEPS_PER_EPOCH = 256 #int(np.load('ICD9/train.npy').shape[0] / 2000)
self . MAX_EPOCHS = 10 if ' max_epochs ' not in args else int ( args [ ' max_epochs ' ] )
@ -533,6 +540,8 @@ class Predict(GNet):
# The code below will insure we have some acceptable cardinal relationships between id and synthetic values
#
df = ( pd . DataFrame ( np . round ( f ) . astype ( np . int32 ) ) )
print ( df . head ( ) )
print ( )
p = 0 not in df . sum ( axis = 1 ) . values
if p :
