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"""
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(c) 2019 Data Maker, hiplab.mc.vanderbilt.edu
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version 1.0.0
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This package serves as a proxy to the overall usage of the framework.
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This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques
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@TODO:
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- Make configurable GPU, EPOCHS
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"""
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import pandas as pd
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import numpy as np
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from multiprocessing import Queue
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self.logger = None
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# logger =
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_args = dict({'ndx':self.ndx,'module':self.name,'table':self.info['from'],'context':_context,'info':_label,**_args})
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# At this point we apply pre-processing of the data if there were ever a need for it
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_index = np.random.choice(np.arange(self._df[name].shape[0]),5,False)
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for values in batches :
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self.log(**{"action":"consolidate","input":{"rows":N,"candidate":_candidates.index(_iodf)}})
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_xdf = _xdf.join(_tmpdf)
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_xdf = _xdf[self._df.columns]
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self._df = _xdf
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pthread= Trainer(**_args)
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