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
                'context':self.info['context'] ,
            - Trainer    -> pre-processing
            - Generation -> post processing
        The specifications of a state space in the configuration file is as such
        state:{pre:{path,pipeline:[]}, post:{path,pipeline:[]}}
        """
        self._states = None
        
        if 'state' in self.info :
            try:
                _config = self.info ['state']
                self._states = State.instance(_config)
            except Exception as e:
                print (e)
                pass
            finally:
                # __info = (pd.DataFrame(self._states)[['name','path','args']]).to_dict(orient='records')
                if self._states :
                    __info = {}
                    
                    for key in self._states :
                        __info[key] = [{"name":_item['name'],"args":_item['args'],"path":_item['path']} for _item in self._states[key]]
                    self.log(object='state-space',action='load',input=__info)
        

        
            # print ()
            _logs = {'action':'status','input':{'pre':self._states['pre']}}
        if self._encoder is None :
        end = datetime.now() #.strftime('%Y-%m-%d %H:%M:%S')
        #
                
            if _item['type'].upper() in ['DATE','DATETIME','TIMESTAMP'] :
                _type = None
        np.random.shuffle(_index)
        :param batch (default 2k)           size of the batch