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