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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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ROUND_UP = 2
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# _BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE)
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#
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if 'gpu' in _args :
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# f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']))
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'context':self.info['context'] ,
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self._encoder = prepare.Input(**_args) if self._df.shape[0] > 0 else None
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if self.gpu :
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if self.gpu :
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# Improve formatting with better post-processing pipeline
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if 'approximate' in self.info :
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_df = self.approximate(_df)
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if 'make_date' in self.info :
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for name in self.info['make_date'] :
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# iname = self.info['make_date']['init_field']
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iname = self.info['make_date'][name]
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years = _df[iname]
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