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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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_args = dict({'ndx':self.ndx,'module':self.name,'table':self.info['from'],'context':_context,'info':_label,**_args})
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NOT_GENERATING = self.name in ['Trainer','Shuffle']
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try:
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super().__init__(**_args)
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writer.write(self._encoder._map,overwrite=True)
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writer.close()
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#
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# @TODO: At this point we need to generate another some other objects
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#
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_args = {"network_args":self.network_args,"store":self.store,"info":self.info,"candidates":self.candidates,"data":self._df}
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class Generator (Learner):
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_args['gpu'] = self.gpu
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FORMAT = self.info['format'][_name]
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self._df = self._df.loc[_index][_ocolumns]
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else:
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