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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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no_value = [type(value) in [int,float,np.int64,np.int32,np.float32,np.float64] for value in self._df[name].values[_index]]
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_queue = caller._queue
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beg = datetime.now() #.strftime('%Y-%m-%d %H:%M:%S')
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if np.sum(index) == 0:
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day = np.random.randint(1,_end)
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FORMAT = '%Y-%m-%d'
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elif 'float' in _df[name].dtypes.name or 'float' in _item['type'].lower():
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_type = np.float
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if _type :
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if _xdf.shape[0] == 0 :
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_xdf = _tmpdf
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else:
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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.start()
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