data-maker/data/gan.py

"""
import pickle
                        self.NUM_LABELS = args['label'].shape[1]
                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.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,
                        "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.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) :
                                                os.mkdir(os.sep.join(root))

                """
        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']
                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            
                Training will consist in having both generator and discriminators
                """
                features_placeholder = tf.compat.v1.placeholder(shape=self._REAL.shape, dtype=tf.float32)
                                                #tf.get_variable_scope().reuse_variables()
                                sess.run(init)
                                                        row = {"logs":logs} #,"model":pickle.dump(sess)}                                                        
                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 = 1000
                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)
                                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
                
                
           
                
                #
                # We should train upon this data