data-maker/data/maker/__init__.py

"""
(c) 2019 Data Maker, hiplab.mc.vanderbilt.edu
version 1.0.0

This package serves as a proxy to the overall usage of the framework.
This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques

@TODO:
    - Make configurable GPU, EPOCHS
"""
import pandas as pd
import numpy as np
    ROUND_UP = 2
        # _BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE)
        #
    if 'gpu' in _args :
        # f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']))
    def run(self):
        self.initalize()
        _space,_matrix = self._encoder.convert()
        
        _args   = self.network_args
        if self.gpu :
            _args['gpu'] = self.gpu
        _args['real'] = _matrix
        _args['candidates'] = self.candidates
        #
        # At this point we have the binary matrix, we can initiate training
        #

        gTrain = gan.Train(**_args)
        gTrain.apply()
       
        writer = transport.factory.instance(provider='file',context='write',path=os.sep.join([gTrain.out_dir,'map.json']))
        writer.write(self._encoder._map,overwrite=True)
        writer.close()

        #
        # @TODO: At this point we need to generate another some other objects
        #
        _args = {"network_args":self.network_args,"store":self.store,"info":self.info,"candidates":self.candidates,"data":self._df}
        if self.gpu :
            _args['gpu'] = self.gpu
        g = Generator(**_args)
        # g.run() 
        self.generate = g
        if self.autopilot :
            self.generate.run()
    def generate (self):
        if self.autopilot :
            print( "Autopilot is set ... No need to call this function")
        else:
            raise Exception( "Autopilot has not been, Wait till training is finished. Use is_alive function on process object")

class Generator (Learner):
    def __init__(self,**_args):
        super().__init__(**_args)
        #
        # We need to load the mapping information for the space we are working with ...
        #
        self.network_args['candidates'] = int(_args['candidates']) if 'candidates' in _args else 1
        filename = os.sep.join([self.network_args['logs'],'output',self.network_args['context'],'map.json'])
        file = open(filename)
        self._map = json.loads(file.read())
        file.close()
    def run(self):
        self.initalize()
        #
        # The values will be returned because we have provided _map information from the constructor
        #
        values,_matrix = self._encoder.convert()
        _args = self.network_args
        _args['map'] = self._map
        _args['values'] = np.array(values)
        _args['row_count'] = self._df.shape[0]
    def make_date(self,**_args) :
            #