data-maker/data/gan.py

"""
This code was originally writen by Ziqi Zhang <ziqi.zhang@vanderbilt.edu> in order to generate synthetic data.
The code is an implementation of a Generative Adversarial Network that uses the Wasserstein Distance (WGAN).
It is intended to be used in 2 modes (embedded in code or using CLI)

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
import numpy as np
import pandas as pd
import time
import os
import sys
from data.params import SYS_ARGS
from data.bridge import Binary
import json
import pickle

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# STEPS_PER_EPOCH         = int(SYS_ARGS['epoch']) if 'epoch' in SYS_ARGS else 256
# NUM_GPUS                        = 1 if 'num_gpu' not in SYS_ARGS else int(SYS_ARGS['num_gpu'])
# BATCHSIZE_PER_GPU   = 2000
# TOTAL_BATCHSIZE         = BATCHSIZE_PER_GPU * NUM_GPUS

class void :
        pass
class GNet :
        def log(self,**args):
                self.logs = dict(args,**self.logs)
           
                        
        """
        This is the base class of a generative network functions, the details will be implemented in the subclasses.
        An instance of this class is accessed as follows 
        object.layers.normalize applies batch normalization or otherwise
        obect.get.variables             instanciate variables on cpu and return a reference (tensor)
        """
        def __init__(self,**args):
                self.layers = void()
                self.layers.normalize = self.normalize
                self.logs = {}

                # self.NUM_GPUS = 1 if 'num_gpu' not in args else args['num_gpu']
                self.GPU_CHIPS = None if 'gpu' not in args else args['gpu']
                if self.GPU_CHIPS is None:
                        self.GPU_CHIPS = [0]
                        if 'CUDA_VISIBLE_DEVICES' in os.environ :
                                os.environ.pop('CUDA_VISIBLE_DEVICES')
                        self.NUM_GPUS = 0
                else:
                        self.NUM_GPUS = len(self.GPU_CHIPS)
                
                self.PARTITION = args['partition']
                # 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 = None
                # 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]
                
                        if args['real'].shape[0]  < PROPOSED_BATCH_PER_GPU :
                                self.BATCHSIZE_PER_GPU = int(args['real'].shape[0]* 1) 
                # 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)       
                self.MAX_EPOCHS = 10 if 'max_epochs' not in args else int(args['max_epochs'])
                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}
                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.logger = args['logger'] if 'logger' in args and args['logger'] else None
                self.init_logs(**args)

        def init_logs(self,**args):
                self.log_dir = args['logs'] if 'logs' in args else 'logs'
                self.mkdir(self.log_dir)
                #
                # 
                for key in ['train','output'] :
                        self.mkdir(os.sep.join([self.log_dir,key]))
                        self.mkdir (os.sep.join([self.log_dir,key,self.CONTEXT]))
                        
                self.train_dir  = os.sep.join([self.log_dir,'train',self.CONTEXT])                
                self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
                # if self.logger :
                        
                #         We will clear the logs from the data-store 
                        
                #         column = self.ATTRIBUTES['synthetic']
                #         db = self.logger.db
                #         if db[column].count() > 0 :
                #                 db.backup.insert({'name':column,'logs':list(db[column].find()) })
                #                 db[column].drop()
                
        def load_meta(self,column):
                """
                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
                """
                # suffix = "-".join(column) if isinstance(column,list)else column
                suffix = self.CONTEXT #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 :
                                value = attr[key]
                                setattr(self,key,value)
                self.train_dir  = os.sep.join([self.log_dir,'train',self.CONTEXT])                
                self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
                                
                        
        def log_meta(self,**args) :
                
                _object = {
                        # '_id':'meta',
                        'CONTEXT':self.CONTEXT,
                        'ATTRIBUTES':self.ATTRIBUTES,
                        'BATCHSIZE_PER_GPU':self.BATCHSIZE_PER_GPU,
                        'Z_DIM':self.Z_DIM,
                        "X_SPACE_SIZE":self.X_SPACE_SIZE,
                        "D_STRUCTURE":self.D_STRUCTURE,
                        "G_STRUCTURE":self.G_STRUCTURE,
                        "NUM_GPUS":self.NUM_GPUS,
                        "GPU_CHIPS":self.GPU_CHIPS,
                        "NUM_LABELS":self.NUM_LABELS,
                        "MAX_EPOCHS":self.MAX_EPOCHS,
                        "ROW_COUNT":self.ROW_COUNT
                }
                if args and 'key' in args and 'value' in args :
                        key = args['key']
                        value= args['value']
                        object[key] = value
                # suffix = "-".join(self.column) if isinstance(self.column,list) else self.column
                suffix = self.CONTEXT #self.get.suffix()
                _name = os.sep.join([self.out_dir,'meta-'+suffix])
                
                f = open(_name+'.json','w')
                f.write(json.dumps(_object))
                return _object
        def mkdir (self,path):
                if not os.path.exists(path) :
                        if os.sep in path :
                                pass
                                root = []
                                for loc in path.split(os.sep) :
                                        root.append(loc)
                                        if not os.path.exists(os.sep.join(root)) :                                                
                                                os.mkdir(os.sep.join(root))

                        elif not os.path.exists(path):
                                os.mkdir(path)            
                

        def normalize(self,**args):
                """
                This function will perform a batch normalization on an network layer
                inputs          input layer of the neural network
                name            name of the scope the 
                labels          labels (attributes not synthesized) by default None
                n_labels        number of labels default None
                """
                inputs  = args['inputs']
                name    = args['name']
                labels  = None if 'labels' not in args else args['labels']
                n_labels= None if 'n_labels' not in args else args['n_labels']
                shift   = [0] if self.__class__.__name__.lower() == 'generator' else [1] #-- not sure what this is doing
                mean, var       = tf.nn.moments(inputs, shift, keep_dims=True)
                shape           = inputs.shape[1].value
                if labels is not None:
                        offset_m        = self.get.variables(shape=[1,shape], name='offset'+name,
                                                                                initializer=tf.zeros_initializer)
                        scale_m = self.get.variables(shape=[n_labels,shape], name='scale'+name,
                                                                        initializer=tf.ones_initializer)
                        offset  = tf.nn.embedding_lookup(offset_m, labels)
                        scale   = tf.nn.embedding_lookup(scale_m, labels)

                else:
                        offset = None
                        scale = None

                result  = tf.nn.batch_normalization(inputs, mean, var,offset,scale, 1e-8)
                return result

        def _variable_on_cpu(self,**args):
                """
                This function makes sure variables/tensors are not created on the GPU but rather on the CPU
                """

                name = args['name']
                shape = args['shape']
                initializer=None if 'initializer' not in args else args['initializer']
                with tf.device('/cpu:0') :
                        cpu_var =  tf.compat.v1.get_variable(name,shape,initializer= initializer)
                return cpu_var
        def average_gradients(self,tower_grads):
                average_grads = []
                for grad_and_vars in zip(*tower_grads):
                        grads = []
                        for g, _ in grad_and_vars:
                                expanded_g = tf.expand_dims(g, 0)
                                grads.append(expanded_g)

                        grad = tf.concat(axis=0, values=grads)
                        grad = tf.reduce_mean(grad, 0)

                        v = grad_and_vars[0][1]
                        grad_and_var = (grad, v)
                        average_grads.append(grad_and_var)
                return average_grads            


class Generator (GNet):
        """
        This class is designed to handle generation of candidate datasets for this it will aggregate a discriminator, this allows the generator not to be random
        
        """
        def __init__(self,**args):
                GNet.__init__(self,**args)
                self.discriminator = Discriminator(**args)
        def loss(self,**args):
                fake    = args['fake']
                label   = args['label']
                y_hat_fake = self.discriminator.network(inputs=fake, label=label)
                #all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
                all_regs = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.REGULARIZATION_LOSSES)
                loss = -tf.reduce_mean(y_hat_fake) + sum(all_regs)
                #tf.add_to_collection('glosses', loss)
                tf.compat.v1.add_to_collection('glosses', loss)
                return loss, loss                
        def load_meta(self, column):
                super().load_meta(column)
                self.discriminator.load_meta(column)
        def network(self,**args) :
                """
                This function will build the network that will generate the synthetic candidates
                :inputs matrix of data that we need
                :dim    dimensions of ...
                """
                x               = args['inputs']
                tmp_dim = self.Z_DIM if 'dim' not in args else args['dim']
                label   = args['label']
                
                with tf.compat.v1.variable_scope('G', reuse=tf.compat.v1.AUTO_REUSE , regularizer=l2_regularizer(0.00001)):
                        for i, dim in enumerate(self.G_STRUCTURE[:-1]):
                                kernel = self.get.variables(name='W_' + str(i), shape=[tmp_dim, dim])
                                h1 = self.normalize(inputs=tf.matmul(x, kernel),shift=0, name='cbn' + str(i), labels=label, n_labels=self.NUM_LABELS)
                                h2 = tf.nn.relu(h1)
                                x = x + h2
                                tmp_dim = dim
                        i = len(self.G_STRUCTURE) - 1
                        #
                        # This seems to be an extra hidden layer: 
                        # It's goal is to map continuous values to discrete values (pre-trained to do this)
                        kernel = self.get.variables(name='W_' + str(i), shape=[tmp_dim, self.G_STRUCTURE[-1]])
                        h1 = self.normalize(inputs=tf.matmul(x, kernel), name='cbn' + str(i),
                                                labels=label, n_labels=self.NUM_LABELS)
                        h2 = tf.nn.tanh(h1)
                        x = x + h2
                        # This seems to be the output layer
                        #
                        kernel = self.get.variables(name='W_' + str(i+1), shape=[self.Z_DIM, self.X_SPACE_SIZE])
                        bias = self.get.variables(name='b_' + str(i+1), shape=[self.X_SPACE_SIZE])
                        x = tf.nn.sigmoid(tf.add(tf.matmul(x, kernel), bias))
                return x           

class Discriminator(GNet):
        def __init__(self,**args):
                GNet.__init__(self,**args)         
        def network(self,**args):
                """
                This function will apply a computational graph on a dataset passed in with the associated labels and the last layer must have a single output (neuron)
                :inputs
                :label
                """
                x = args['inputs']
                label = args['label']
                with tf.compat.v1.variable_scope('D', reuse=tf.compat.v1.AUTO_REUSE , regularizer=l2_regularizer(0.00001)):
                        for i, dim in enumerate(self.D_STRUCTURE[1:]):
                                kernel = self.get.variables(name='W_' + str(i), shape=[self.D_STRUCTURE[i], dim])
                                bias = self.get.variables(name='b_' + str(i), shape=[dim])
                                # print (["\t",bias,kernel])
                                x = tf.nn.relu(tf.add(tf.matmul(x, kernel), bias))
                                x = self.normalize(inputs=x, name='cln' + str(i), shift=1,labels=label, n_labels=self.NUM_LABELS)
                        i = len(self.D_STRUCTURE)
                        kernel = self.get.variables(name='W_' + str(i), shape=[self.D_STRUCTURE[-1], 1])
                        bias = self.get.variables(name='b_' + str(i), shape=[1])
                        y = tf.add(tf.matmul(x, kernel), bias)
                return y
        
        def loss(self,**args) :
                """
                This function compute the loss of 
                :real
                :fake
                :label
                """
                real    = args['real']
                fake    = args['fake']
                label   = args['label']
                epsilon = tf.random.uniform(shape=[self.BATCHSIZE_PER_GPU,1],minval=0,maxval=1)
                
                x_hat           = real + epsilon * (fake - real)
                y_hat_fake      = self.network(inputs=fake, label=label)
                
                y_hat_real      = self.network(inputs=real, label=label)
                y_hat           = self.network(inputs=x_hat, label=label)

                grad            = tf.gradients(y_hat, [x_hat])[0]
                slopes          = tf.sqrt(tf.reduce_sum(tf.square(grad), 1))
                gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
                #all_regs        = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
                all_regs        = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.REGULARIZATION_LOSSES)
                w_distance      = -tf.reduce_mean(y_hat_real) + tf.reduce_mean(y_hat_fake)
                loss            = w_distance + 10 * gradient_penalty + sum(all_regs)
                #tf.add_to_collection('dlosses', loss)
                tf.compat.v1.add_to_collection('dlosses', loss)

                return w_distance, loss            
class Train (GNet):
        def __init__(self,**args):
                GNet.__init__(self,**args)
                self.generator = Generator(**args)
                self.discriminator = Discriminator(**args)
                self._REAL = args['real']
                self._LABEL= args['label'] if 'label' in args else None
                # self.column = args['column']
                # print ([" *** ",self.BATCHSIZE_PER_GPU])
                
                self.meta = self.log_meta()
                if(self.logger):
                        
                        self.logger.write({"module":"gan-train","action":"start","input":{"partition":self.PARTITION,"meta":self.meta} } )
                
                # self.log (real_shape=list(self._REAL.shape),label_shape = self._LABEL.shape,meta_data=self.meta)
        def load_meta(self, column):
                """
                This function will delegate the calls to load meta data to it's dependents
                column name
                """
                super().load_meta(column)
                self.generator.load_meta(column)
                self.discriminator.load_meta(column)
        def loss(self,**args):
                """
                This function will compute a "tower" loss of the generated candidate against real data
                Training will consist in having both generator and discriminators
                :scope
                :stage
                :real
                :label
                """

                scope   = args['scope']
                stage   = args['stage']
                real    = args['real']
                label   = args['label']

                
                if label is not None :
                        label   = tf.cast(label, tf.int32)
                        #
                        # @TODO: Ziqi needs to explain what's going on here
                        m = [[i] for i in np.arange(self._LABEL.shape[1]-2)]
                        label   = label[:, 1] * len(m) + tf.squeeze(
                                tf.matmul(label[:, 2:], tf.constant(m, dtype=tf.int32))
                                )
                # label = label[:,1] * 4 + tf.squeeze( label[:,2]*[[0],[1],[2],[3]] )
                z = tf.random.normal(shape=[self.BATCHSIZE_PER_GPU, self.Z_DIM])
                
                fake = self.generator.network(inputs=z, label=label)
                if stage == 'D':
                        w, loss = self.discriminator.loss(real=real, fake=fake, label=label)
                        #losses = tf.get_collection('dlosses', scope)
                        flag = 'dlosses'
                        losses = tf.compat.v1.get_collection('dlosses', scope)
                else:
                        w, loss = self.generator.loss(fake=fake, label=label)
                        #losses = tf.get_collection('glosses', scope)
                        flag = 'glosses'
                        losses = tf.compat.v1.get_collection('glosses', scope)
                # losses = tf.compat.v1.get_collection(flag, scope)

                total_loss = tf.add_n(losses, name='total_loss')
                # print (total_loss)
                return total_loss, w
        def input_fn(self):
                """
                This function seems to produce 
                """
                features_placeholder = tf.compat.v1.placeholder(shape=self._REAL.shape, dtype=tf.float32)
                LABEL_SHAPE = [None,None] if self._LABEL is None else self._LABEL.shape
                labels_placeholder = tf.compat.v1.placeholder(shape=LABEL_SHAPE, dtype=tf.float32)
                if self._LABEL is not None :
                        dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
                else :
                        dataset = tf.data.Dataset.from_tensor_slices(features_placeholder)
                # labels_placeholder = None
                dataset = dataset.repeat(10000)
                
                dataset = dataset.batch(batch_size=self.BATCHSIZE_PER_GPU)
                dataset = dataset.prefetch(1)
                # iterator = dataset.make_initializable_iterator()
                iterator = tf.compat.v1.data.make_initializable_iterator(dataset)
                return iterator, features_placeholder, labels_placeholder
        
        def network(self,**args):
                stage   = args['stage']
                opt     = args['opt']
                tower_grads = []
                per_gpu_w   = []
                iterator, features_placeholder, labels_placeholder = self.input_fn()
                with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope()):
                        #
                        # @TODO: Find a way to handle this across multiple CPU in case the GPU are not available
                        #       - abstract hardware specification
                        #       - determine if the GPU/CPU are busy
                        #
                        for i in self.GPU_CHIPS : #range(self.NUM_GPUS):
                                with tf.device('/gpu:%d' % i):
                                        with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
                                                if self._LABEL is not None :
                                                        (real, label) = iterator.get_next()
                                                else:
                                                        real = iterator.get_next()
                                                        label= None
                                                loss, w = self.loss(scope=scope, stage=stage, real=real, label=label)
                                                #tf.get_variable_scope().reuse_variables()
                                                tf.compat.v1.get_variable_scope().reuse_variables()
                                                #vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
                                                vars_ = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
                                                grads = opt.compute_gradients(loss, vars_)
                                                tower_grads.append(grads)
                                                per_gpu_w.append(w)

                grads = self.average_gradients(tower_grads)
                apply_gradient_op = opt.apply_gradients(grads)

                mean_w = tf.reduce_mean(per_gpu_w)
                train_op = apply_gradient_op
                return train_op, mean_w, iterator, features_placeholder, labels_placeholder
        def apply(self,**args):
                # max_epochs = args['max_epochs'] if 'max_epochs' in args else 10
                REAL = self._REAL
                LABEL= self._LABEL       
                if (self.logger):
                        pass
                
                with tf.device('/cpu:0'):
                        opt_d = tf.compat.v1.train.AdamOptimizer(1e-4)
                        opt_g = tf.compat.v1.train.AdamOptimizer(1e-4)
                        
                        train_d, w_distance, iterator_d, features_placeholder_d, labels_placeholder_d = self.network(stage='D', opt=opt_d)
                        train_g, _, iterator_g, features_placeholder_g, labels_placeholder_g = self.network(stage='G', opt=opt_g)
                        # saver = tf.train.Saver()
                        saver   = tf.compat.v1.train.Saver()
                        # init    = tf.global_variables_initializer()
                        init    = tf.compat.v1.global_variables_initializer()
                        logs = []
                        #with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
                        with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
                                
                                sess.run(init)
                                
                                sess.run(iterator_d.initializer,
                                                        feed_dict={features_placeholder_d: REAL})
                                sess.run(iterator_g.initializer,
                                                        feed_dict={features_placeholder_g: REAL})
                                
                                for epoch in range(1, self.MAX_EPOCHS + 1):
                                        start_time = time.time()
                                        w_sum = 0
                                        for i in range(self.STEPS_PER_EPOCH):
                                                for _ in range(2):
                                                        _, w = sess.run([train_d, w_distance])
                                                        w_sum += w
                                                sess.run(train_g)
                                        duration = time.time() - start_time

                                        assert not np.isnan(w_sum), 'Model diverged with loss = NaN'

                                        format_str = 'epoch: %d, w_distance = %f (%.1f)'
                                        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) })

                                        # if epoch % self.MAX_EPOCHS == 0:
                                        if epoch in [5,10,20,50,75, self.MAX_EPOCHS] :
                                                # suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
                                                suffix = self.CONTEXT #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)
                                                #
                                                #
                                                if self.logger :
                                                        row = {"module":"gan-train","action":"logs","input":{"partition":self.PARTITION,"logs":logs}} #,"model":pickle.dump(sess)}                                                        
                                                        self.logger.write(row)
                                                        #
                                                        # @TODO:
                                                        # We should upload the files in the checkpoint 
                                                        # This would allow the learnt model to be portable to another system
                                                        #
                        tf.compat.v1.reset_default_graph()

class Predict(GNet):
        """
        This class uses synthetic data given a learned model
        """
        def __init__(self,**args):
                GNet.__init__(self,**args)                
                self.generator  = Generator(**args)                
                self.values     = args['values']
                self.ROW_COUNT  = args['row_count']
                self.oROW_COUNT = self.ROW_COUNT
                # self.MISSING_VALUES = np.nan_to_num(np.nan)
                # if 'no_value' in args and args['no_value'] not in ['na','','NA'] :
                #         self.MISSING_VALUES = args['no_value']
                self.MISSING_VALUES = args['missing'] if 'missing' in args else []
                
                        
                # self.MISSING_VALUES = args['no_value']
                # self.MISSING_VALUES = int(args['no_value']) if args['no_value'].isnumeric() else  np.na if args['no_value'] in ['na','NA','N/A'] else args['no_value']
        def load_meta(self, column):
                super().load_meta(column)
                self.generator.load_meta(column)
                self.ROW_COUNT = self.oROW_COUNT
        def apply(self,**args):
                suffix = self.CONTEXT #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']
                #
                # setup computational graph
                tf.compat.v1.reset_default_graph()
                z = tf.random.normal(shape=[self.ROW_COUNT, self.Z_DIM])

                y = tf.compat.v1.placeholder(shape=[self.ROW_COUNT, self.NUM_LABELS], dtype=tf.int32)
                if self._LABEL is not None :
                        ma = [[i] for i in np.arange(self.NUM_LABELS - 2)]
                        label = y[:, 1] * len(ma) + tf.squeeze(tf.matmul(y[:, 2:], tf.constant(ma, dtype=tf.int32)))
                else:
                        label = None

                fake    = self.generator.network(inputs=z, label=label)
                init    = tf.compat.v1.global_variables_initializer()
                saver   = tf.compat.v1.train.Saver()
                df              = pd.DataFrame()
                CANDIDATE_COUNT = args['candidates'] if 'candidates' in  args else 1 #0 if self.ROW_COUNT < 1000 else 100
                candidates = []

                with tf.compat.v1.Session() as sess:
                        saver.restore(sess, model_dir)
                        if self._LABEL is not None :
                                # labels = np.zeros((self.ROW_COUNT,self.NUM_LABELS) )
                                labels= demo
                        else:
                                labels = None
                        
                        for i in np.arange(CANDIDATE_COUNT) :
                                if labels :
                                        _matrix = sess.run(fake,feed_dict={y:labels})
                                else:
                                        _matrix = sess.run(fake)
                                #
                                # if we are dealing with numeric values only we can perform a simple marginal sum against the indexes
                                # 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)
                                # candidates.append (np.round(_matrix).astype(np.int64)) 
                                candidates.append(np.array([np.round(row).astype(int) for row in _matrix]))                       
                # return candidates[0] if len(candidates) == 1 else candidates
                
                return candidates 

        def _apply(self,**args):
                # print (self.train_dir)
                # suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
                suffix = self.CONTEXT #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.ROW_COUNT, self.Z_DIM])

                y = tf.compat.v1.placeholder(shape=[self.ROW_COUNT, self.NUM_LABELS], dtype=tf.int32)
                if self._LABEL is not None :
                        ma = [[i] for i in np.arange(self.NUM_LABELS - 2)]
                        label = y[:, 1] * len(ma) + tf.squeeze(tf.matmul(y[:, 2:], tf.constant(ma, dtype=tf.int32)))
                else:
                        label = None

                fake    = self.generator.network(inputs=z, label=label)
                init    = tf.compat.v1.global_variables_initializer()
                saver   = tf.compat.v1.train.Saver()
                df              = pd.DataFrame()
                CANDIDATE_COUNT = 5 #0 if self.ROW_COUNT < 1000 else 100
                NTH_VALID_CANDIDATE = count = np.random.choice(np.arange(2,60),2)[0]
                with tf.compat.v1.Session() as sess:
                        
                        # sess.run(init)

                        saver.restore(sess, model_dir)
                        if self._LABEL is not None :
                                labels = np.zeros((self.ROW_COUNT,self.NUM_LABELS) )
                                labels= demo
                        else:
                                labels = None
                        
                        found = []
                        ratio = []
                        __x__ = None
                        __ratio=0
                        for i in np.arange(CANDIDATE_COUNT) :
                                if labels :
                                        _matrix = sess.run(fake,feed_dict={y:labels})
                                else:
                                        _matrix = sess.run(fake)
                                #
                                # if we are dealing with numeric values only we can perform a simple marginal sum against the indexes
                                # 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)
                                found.append (np.round(_matrix).astype(np.int64))
                                # df =   pd.DataFrame(np.round(_matrix),dtype=int)
                                p = 0 not in df.sum(axis=1).values
                                # x = df.sum(axis=1).values
                                
                                # if np.divide( np.sum(x), x.size)  > .9 or p and np.sum(x) == x.size :
                                #         ratio.append(np.divide( np.sum(x), x.size))
                                #         found.append(df)
                                        
                                #         # break
                                #         if len(found) == CANDIDATE_COUNT:
                                                
                                #                 break
                                # else:
                                #         __x__   = df if  __x__ is None or np.where(x > 0)[0].size > np.where(__x__ > 0)[0].size else __x__
                                #         __ratio = np.divide( np.sum(x), x.size) if __x__ is None or np.where(x > 0)[0].size > np.where(__x__ > 0)[0].size else __ratio
                                #         continue
                                       
                        # 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 
                        #
                        # N = len(found)
                        # _index = [i for i in range(0,N) if found[i].shape[1] == len(self.values)]
                        # if not _index and not found :
                        #         df = __x__
                        #         INDEX = -1
                        # else :
                        #         if not _index :
                        #                 INDEX = np.random.choice(np.arange(len(found)),1)[0]
                        #                 INDEX = ratio.index(np.max(ratio))
                        #         else:
                        #                 INDEX = _index[0]

                        
                        #         df = found[INDEX]
                        # columns = self.ATTRIBUTES['synthetic'] if isinstance(self.ATTRIBUTES['synthetic'],list)else [self.ATTRIBUTES['synthetic']]
                        
                        # r = np.zeros((self.ROW_COUNT,len(columns)))
                        # r = np.zeros(self.ROW_COUNT)
                        
                        # if self.logger :
                        #         info = {"found":len(found),"rows":df.shape[0],"cols":df.shape[1],"expected":len(self.values)}
                        #         if df.shape[1] > len(self.values) :
                        #                 df = df.iloc[:len(self.values)]
                        #         if INDEX > 0 :
                        #                 info =dict(info ,**{"selected":INDEX, "ratio": ratio[INDEX] })
                        #         else :
                                        
                        #                 info['selected'] = -1
                        #                 info['ratio'] = __ratio
                        #         info['partition'] = self.PARTITION
                        #         self.logger.write({"module":"gan-generate","action":"generate","input":info})
                        # # df.columns = self.values
                        # if len(found) or df.columns.size <= len(self.values):
                        #         ii = df.apply(lambda row: np.sum(row) == 0 ,axis=1)
                        #         missing = []
                        #         if ii.sum() > 0 :
                        #                 #
                        #                 # If the generator had a reductive effect we should be able to get random values from either :
                        #                 #       - The space of outliers
                        #                 #       - existing values for smaller spaces that have suffered over training
                        #                 #

                        #                 N = ii.sum() 
                        #                 missing_values = self.MISSING_VALUES if self.MISSING_VALUES else self.values
                        #                 missing = np.random.choice(missing_values,N)
                        #                 # missing = []
                        #         #
                        #         # @TODO:
                        #         #       Log the findings here in terms of ratio, missing, candidate count
                        #         # print ([np.max(ratio),len(missing),len(found),i])
                        #         i = np.where(ii == 0)[0]
                                
                                
                        #         df =  pd.DataFrame( df.iloc[i].apply(lambda row: self.values[np.random.choice(np.where(row != 0)[0],1)[0]] ,axis=1))
                        #         df.columns = columns
                        #         df = df[columns[0]].append(pd.Series(missing))
                                
                                
                        #         if self.logger :
                                        
                        #                 info= {"missing": i.size,"rows":df.shape[0],"cols":1,'partition':self.PARTITION}
                        #                 self.logger.write({"module":"gan-generate","action":"compile.io","input":info})

                           
                        
                # print(df.head())
                tf.compat.v1.reset_default_graph()
                # df = pd.DataFrame(df)
                # df.columns = columns
                # np.random.shuffle(df[columns[0]].values)
                # return df.to_dict(orient='list')
                return _matrix


if __name__ == '__main__' :
        #
        # Now we get things done ...
        column          = SYS_ARGS['column']
        column_id       = SYS_ARGS['id'] if 'id' in SYS_ARGS else 'person_id'
        column_id       = column_id.split(',') if ',' in column_id else column_id
        df = pd.read_csv(SYS_ARGS['raw-data'])  
        LABEL = pd.get_dummies(df[column_id]).astype(np.float32).values
        
        context         = SYS_ARGS['raw-data'].split(os.sep)[-1:][0][:-4]
        if set(['train','learn']) & set(SYS_ARGS.keys()):
                
                df = pd.read_csv(SYS_ARGS['raw-data'])   
                
                # cols = SYS_ARGS['column']
                # _map,_df = (Binary()).Export(df)
                # i = np.arange(_map[column]['start'],_map[column]['end'])
                max_epochs = np.int32(SYS_ARGS['max_epochs']) if 'max_epochs' in SYS_ARGS else 10
                # REAL    = _df[:,i]
                REAL    = pd.get_dummies(df[column]).astype(np.float32).values
                LABEL = pd.get_dummies(df[column_id]).astype(np.float32).values
                trainer = Train(context=context,max_epochs=max_epochs,real=REAL,label=LABEL,column=column,column_id=column_id)
                trainer.apply()
                
                
           
                
                #
                # We should train upon this data
                #
                # -- we need to convert the data-frame to binary matrix, given a column
                #
                pass
        elif 'generate' in SYS_ARGS:
                values = df[column].unique().tolist()
                values.sort()
                
                p = Predict(context=context,label=LABEL,values=values,column=column)
                p.load_meta(column)
                r = p.apply()
                # print (df)
                # print ()
                df[column] = r[column]
                # print (df)
                
                
        else:
                print (SYS_ARGS.keys())
                print (__doc__)
        pass