You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
278 lines
11 KiB
Python
278 lines
11 KiB
Python
"""
|
|
(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
|
|
# import cupy as cp
|
|
import sys
|
|
import os
|
|
#
|
|
# The following is to address the issue over creating a large matrix ...
|
|
#
|
|
# from multiprocessing import Process, Queue
|
|
|
|
# if 'GPU' in os.environ :
|
|
# import cupy as np
|
|
# else:
|
|
# import numpy as np
|
|
class void:
|
|
pass
|
|
class Hardware :
|
|
"""
|
|
This class is intended to allow the use of hardware i.e GPU, index or CPU
|
|
"""
|
|
pass
|
|
|
|
class Input :
|
|
"""
|
|
This class is designed to read data from a source and and perform a variet of operations :
|
|
- provide a feature space, and rows (matrix profile)
|
|
- a data index map
|
|
"""
|
|
|
|
def __init__(self,**_args):
|
|
"""
|
|
:param data
|
|
:param store data-store parameters/configuration
|
|
:param sql sql query that pulls a representative sample of the data
|
|
"""
|
|
self._schema = _args['schema'] if 'schema' in _args else {}
|
|
#
|
|
# schema data should be in a hash map for these purposes
|
|
#
|
|
# if self._schema :
|
|
# r = {}
|
|
# for _item in self._schema :
|
|
# r[_item['name']] = r[_item['type']]
|
|
# self._schema = r
|
|
|
|
self.df = _args['data']
|
|
if 'sql' not in _args :
|
|
self._initdata(**_args)
|
|
#
|
|
pass
|
|
else:
|
|
self._initsql(**_args)
|
|
#
|
|
# We need to have a means to map of values,columns and vector positions in order
|
|
# to perform convert and revert to and from binary
|
|
#
|
|
self._map = {} if 'map' not in _args else _args['map']
|
|
|
|
|
|
def _initsql(self,**_args):
|
|
"""
|
|
This function will initialize the class on the basis of a data-store and optionally pre-defined columns to be used to be synthesized
|
|
:param store data-store configuration
|
|
:param columns list of columns to be
|
|
"""
|
|
|
|
if 'columns' not in _args :
|
|
self._initcols(data=self.df)
|
|
else:
|
|
self._initcols(data=self.df,columns=_args['columns'])
|
|
|
|
pass
|
|
def _init_map(self,values):
|
|
self._map = dict(zip(np.arange(len(values)),values))
|
|
for key in self._map :
|
|
self._map[key] = self._map[key].tolist()
|
|
def _initcols (self,**_args) :
|
|
"""
|
|
This function will initialize the columns to be synthesized and/or determine which ones can be synthesized
|
|
:param data data-frame that holds the data (matrix)
|
|
:param columns optional columns to be synthesized
|
|
"""
|
|
# df = _args['data'].copy()
|
|
row_count = self.df.shape[0]
|
|
cols = None if 'columns' not in _args else _args['columns']
|
|
self.columns = self.df.columns.tolist()
|
|
self._io = []
|
|
|
|
if 'columns' in _args :
|
|
self._columns = _args['columns']
|
|
# else:
|
|
#
|
|
# We will look into the count and make a judgment call
|
|
try:
|
|
# _df = pd.DataFrame(self.df.apply(lambda col: col.dropna().unique().size )).T
|
|
# MIN_SPACE_SIZE = 2
|
|
# self._columns = cols if cols else _df.apply(lambda col:None if col[0] == row_count or col[0] < MIN_SPACE_SIZE else col.name).dropna().tolist()
|
|
# self._io = _df.to_dict(orient='records')
|
|
_df = pd.DataFrame(self.df.nunique().T / self.df.shape[0]).T
|
|
self._io = (_df.to_dict(orient='records'))
|
|
|
|
except Exception as e:
|
|
print (e)
|
|
self._io = []
|
|
def _initdata(self,**_args):
|
|
"""
|
|
This function will initialize the class with a data-frame and columns of interest (if any)
|
|
:param data data-frame that holds the data
|
|
:param columns columns that need to be synthesized if any
|
|
"""
|
|
self._initcols(**_args)
|
|
|
|
def _convert(self,**_args):
|
|
"""
|
|
This function will convert a data-frame into a binary matrix and provide a map to be able to map the values back to the matrix
|
|
:param columns in case we specify the columns to account for (just in case the original assumptions don't hold)
|
|
"""
|
|
if 'columns' in _args or 'column' in _args :
|
|
columns = _args['columns'] if 'columns' in _args else [_args['column']]
|
|
else:
|
|
columns = self._columns
|
|
_df = self.df if 'data' not in _args else _args['data']
|
|
#
|
|
# At this point we have the list of features we want to use
|
|
i = 0
|
|
|
|
_m = np.array([])
|
|
_values = []
|
|
for name in columns :
|
|
#
|
|
# In case we have dataset with incomplete value space, we should still be able to generate something meaningful
|
|
#
|
|
values = None if name not in self._map else list(self._map[name]['values'])
|
|
_type = self._schema[name] if name in self._schema else _df[name].dtype
|
|
cols, _matrix = self.tobinary(_df[name],values)
|
|
_beg,_end = i,i+len(cols)
|
|
if name not in self._map :
|
|
self._map[name] = {"beg":_beg,"end":_end ,"values":cols.tolist()}
|
|
i += len(cols)
|
|
if not _m.shape[0]:
|
|
_m = _matrix ;
|
|
else:
|
|
_m = np.concatenate((_m,_matrix),axis=1)
|
|
if values :
|
|
_values += list(values)
|
|
#
|
|
# @NOTE:
|
|
# The map should allow us to be able to convert or reconvert the binary matrix to whatever we want ...
|
|
#
|
|
# self._matrix = _m
|
|
|
|
return _values,_m
|
|
|
|
def _revert(self,**_args) :
|
|
"""
|
|
This function will take in a binary matrix and based on the map of values it will repopulate it with values
|
|
:param _matrix binary matrix
|
|
:param column|columns column name or columns if the column is specified
|
|
"""
|
|
_column = _args['column'] if 'column' in _args else None
|
|
|
|
|
|
matrix = _args['matrix']
|
|
row_count = matrix.shape[0]
|
|
r = {}
|
|
for key in self._map :
|
|
if _column and key != _column :
|
|
continue
|
|
_item = self._map[key]
|
|
_beg = _item['beg']
|
|
_end = _item['end']
|
|
columns = np.array(_item['values'])
|
|
#
|
|
# @NOTE: We are accessing matrices in terms of [row,col],
|
|
# 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]
|
|
#
|
|
# we should consider decoding the matrix if possible
|
|
#
|
|
|
|
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)
|
|
row_count = np.int64(len(rows))
|
|
# if 'GPU' not in os.environ :
|
|
# _matrix = np.zeros([row_count,cols.size],dtype=int)
|
|
#
|
|
# @NOTE: For some reason, there is an out of memory error created here, this seems to fix it (go figure)
|
|
#
|
|
_matrix = np.array([np.repeat(0,cols.size) for i in range(0,row_count)])
|
|
|
|
[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]
|
|
# else:
|
|
# _matrix = cp.zeros([row_count,cols.size])
|
|
# [cp.put(_matrix[i], cp.where(cols == rows[i]),1)for i in cp.arange(row_count) ]
|
|
# _matrix = _matrix.asnumpy()
|
|
|
|
|
|
return cols,_matrix
|
|
def convert(self,**_args):
|
|
if 'columns' in _args or 'column' in _args :
|
|
columns = _args['columns'] if 'columns' in _args else [_args['column']]
|
|
else:
|
|
columns = self._columns
|
|
_df = self.df if 'data' not in _args else _args['data']
|
|
_values,_matrix = self.encode(_df,columns)
|
|
_, _matrix = self.tobinary(_matrix)
|
|
self._init_map(_values)
|
|
return _values,_matrix #-- matrix has been updated !
|
|
def revert(self,**_args):
|
|
# _columns = _args['column'] if 'column' in _args else None
|
|
_matrix = _args['matrix']
|
|
# print (_matrix)
|
|
return self.decode(_matrix,columns=self._columns)
|
|
pass
|
|
def encode(self,df,columns) :
|
|
_df = df[columns].drop_duplicates()
|
|
_values = _df.values.tolist()
|
|
_encoded = df[columns].apply(lambda row: _values.index( list(row)) ,axis=1)
|
|
return np.array(_values),_encoded
|
|
def decode (self,_matrix,**_args):
|
|
#
|
|
# _matrix binary matrix
|
|
# _values value space given the columns
|
|
# columns name of the columns ...
|
|
#
|
|
|
|
columns = _args['columns']
|
|
_values = np.array( list(self._map.values()))
|
|
_matrix = pd.DataFrame(_matrix) #if type(_matrix) != pd.DataFrame else _matrix
|
|
# x = _matrix.apply(lambda row: _values[row.values == 1 ].tolist()[0] if row.values.sum() > 0 else None, axis=1).tolist()
|
|
#@TODO: Provide random values for things that are missing
|
|
|
|
# x = _matrix.apply(lambda row: _values[row.values == 1].tolist()[0] if (row.values == 1).sum() > 0 else np.repeat(None,len(self._columns)) ,axis=1).tolist()
|
|
novalues = _values[np.random.choice(_values.size,1)[0]].tolist()
|
|
# novalues = np.repeat(None,len(self._columns))
|
|
x = _matrix.apply(lambda row: _values[row.values == 1].tolist()[0] if (row.values == 1).sum() > 0 else novalues ,axis=1).tolist()
|
|
return pd.DataFrame(x,columns=columns)
|
|
|
|
|
|
|