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
from multiprocessing import Queue
        self.logger = None
            # logger = 
            _args = dict({'ndx':self.ndx,'module':self.name,'table':self.info['from'],'context':_context,'info':_label,**_args})
        # At this point we apply pre-processing of the data if there were ever a need for it
            no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index]]            
        pass
        gTrain = gan.Train(**_args)
        gTrain.apply()
       
            print( "Autopilot is set ... No need to call this function")
        _args['row_count'] = self._df.shape[0]
                    # Sometimes messy data has unpleasant surprises
        FORMAT =  '%Y-%m-%d'
                
            #         # iname = self.info['make_date']['init_field']
        

        _xdf = _xdf[self._df.columns]            
        self._df = _xdf
        return pthread