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"""
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(c) 2018 - 2021, Vanderbilt University Medical Center
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Steve L. Nyemba, steve.l.nyemba@vumc.org
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This file is designed to handle preconditions for a generative adversarial network:
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- The file will read/get data from a source specified by transport (or data-frame)
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- The class will convert the data to a binary vector
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- The class will also help rebuild the data from a binary matrix.
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Usage :
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"""
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import transport
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import json
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import pandas as pd
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import numpy as np
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def __init__(self,**_args):
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:param _matrix binary matrix
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:param column|columns column name or columns if the column is specified
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"""
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_column = _args['column'] if 'column' in _args else None
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matrix = _args['matrix']
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row_count = matrix.shape[0]
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r = {}
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for key in self._map :
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if _column and key != _column :
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continue
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_item = self._map[key]
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_beg = _item['beg']
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_end = _item['end']
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columns = np.array(_item['values'])
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#
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# @NOTE: We are accessing matrices in terms of [row,col],
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# The beg,end variables are for the columns in the matrix (mini matrix)
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#
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# if not _column :
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# _matrix = matrix[:,_beg:_end] #-- The understanding is that _end is not included
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# else:
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# _matrix = matrix
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_matrix = matrix[:,_beg:_end]
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#
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# vectorize the matrix to replace the bits by their actual values (accounting for the data-types)
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# @TODO: Find ways to do this on a GPU (for big data) or across threads
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#
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row_count = _matrix.shape[0]
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# r[key] = [columns[np.where(row == 1) [0][0] ] for row in _matrix[:,_beg:_end]]
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r[key] = [columns[np.where(row==1)[0][0]] if np.where(row==1)[0].size > 0 else '' for row in _matrix]
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return pd.DataFrame(r)
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def tobinary(self,rows,cols=None) :
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"""
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This function will compile a binary matrix from a row of values this allows hopefully this can be done in parallel, this function can be vectorized and processed
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:param rows np.array or list of vector of values
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:param cols a space of values if it were to be different fromt he current sample.
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"""
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if not cols:
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#
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# In the advent the sample rows do NOT have the values of the
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cols = rows.unique()
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cols = np.array(cols)
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