repo stringlengths 3 91 | file stringlengths 16 152 | code stringlengths 0 3.77M | file_length int64 0 3.77M | avg_line_length float64 0 16k | max_line_length int64 0 273k | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
Beholder-GAN | Beholder-GAN-master/metrics/sliced_wasserstein.py | #Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
#
#Attribution-NonCommercial 4.0 International
#
#=======================================================================
#
#Creative Commons Corporation ("Creative Commons") is not a law firm and
#does not provide legal services or legal advice. Distribut... | 25,263 | 45.698706 | 135 | py |
Beholder-GAN | Beholder-GAN-master/metrics/frechet_inception_distance.py | #!/usr/bin/env python3
#
# Copyright 2017 Martin Heusel
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable la... | 11,441 | 39.574468 | 110 | py |
Beholder-GAN | Beholder-GAN-master/metrics/ms_ssim.py | #!/usr/bin/python
#
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | 8,160 | 39.60199 | 128 | py |
Beholder-GAN | Beholder-GAN-master/metrics/inception_score.py | # Copyright 2016 Wojciech Zaremba
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | 5,305 | 34.851351 | 110 | py |
Beholder-GAN | Beholder-GAN-master/metrics/__init__.py | # empty
| 8 | 3.5 | 7 | py |
Beholder-GAN | Beholder-GAN-master/utils/plot_beauty_distribution.py | import os
import csv
import numpy as np
import argparse
import matplotlib.pyplot as plt
# initialize parser arguments
parser = argparse.ArgumentParser()
parser.add_argument('--csv', '-csv', help='path to csv file', default='../All_Ratings.csv', type=str)
parser.add_argument('--density', '-density', help='configure plo... | 1,767 | 30.017544 | 101 | py |
Beholder-GAN | Beholder-GAN-master/utils/transform_images.py | import os
from PIL import Image
# select dataset folder to check and destination folder to put output images in
path = '../datasets/beauty_dataset/img/beauty_dataset'
dest_path = '../datasets/beauty_dataset/img/beauty_dataset_scaled'
# destination resolution
dest_res = 2 ** 8
for i, file in enumerate(os.listdir(path... | 1,274 | 35.428571 | 86 | py |
Themis | Themis-master/Themis1.0/main.py | import math
import random
import sys
import xml.etree.ElementTree as ET
import Themis
def load_soft_from_settings():
names=[]
types=[]
values=[]
num_values=[]
tree = ET.parse('settings.xml')
root = tree.getroot()
software_name = root.find("name").text
command = root.find("command").te... | 1,506 | 27.433962 | 83 | py |
Themis | Themis-master/Themis1.0/Themis.py | import sys
import itertools
import commands
import random
import math
import copy
class soft:
conf_zValue = {80:1.28,90:1.645,95:1.96, 98:2.33, 99:2.58}
MaxSamples=50
SamplingThreshold = 10
cache = {}
def __init__(self, names, values, num, command, type):
self.attr_names = copy.deepcopy(n... | 8,590 | 27.44702 | 100 | py |
Themis | Themis-master/Themis1.0/software.py | import sys
sex = sys.argv[1]
race = sys.argv[3]
if(sex=="Male" and race=="Red"):
print "1"
else:
print "0"
| 116 | 12 | 32 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/wrapper.py | '''
Wrapper script to call each of the subject system depending on the input arguments
'''
import sys
import commands
'''
Usage :
argv[1] : Name of the subjecct system
argv[2] : The dataset to train the classifier
argv[3] : Type of discrimination (Group/Causal)
argv[4] : The sensitive argument to train the classfier
... | 5,896 | 23.26749 | 82 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for race
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 1,106 | 17.762712 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/Table1GroupScore.py | '''
This script calculates the Group discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 1,077 | 17.912281 | 107 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/A/Atestcredit.py | '''
Test the Subject System A on Credit dataset to generate the output for the input given as argv arguments.
All the inputs are assumed to be space separated.
'''
from __future__ import division
from random import seed, shuffle
import random
import math
import os
from collections import defaultdict
from sklearn import... | 1,801 | 20.97561 | 128 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/A/ACausalGender.py | '''
Causal discrimination testing for Subject System A
Inputs :
argv[1] : Train file
argv[2] : Sensitive argument
8 means race and 9 means gender
'''
from __future__ import division
from random import seed, shuffle
import random
import math
import os
from collections import defaultdict
from sklear... | 6,388 | 25.620833 | 127 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/A/ACausalRace.py | '''
Group discrimination testing for Subject System A
Inputs :
argv[1] : Train file
argv[2] : Sensitive argument
argv[3] : Argument to test discriminationa gainst
For argv[2] and argv[3] : 8 means race and 9 means gender
'''
from __future__ import division
from random import seed, shuffle
import random
... | 6,473 | 25.752066 | 127 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/A/ACausalCredit.py | '''
Causal discrimination testing for Subject System A on Credit dataset
Inputs :
argv[1] : Train file
'''
from __future__ import division
from random import seed, shuffle
import random
import math
import os
from collections import defaultdict
from sklearn import svm
import os,sys
import urllib2
sys.path.insert(0, ... | 6,127 | 25.643478 | 127 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/A/AGroupCredit.py | '''
Group discrimination testing for Subject System A for Credit dataset
Inputs :
argv[1] : Train file
'''
from __future__ import division
from random import seed, shuffle
import random
import math
import os
from collections import defaultdict
from sklearn import svm
import os,sys
import urllib2
sys.path.insert(0, ... | 5,298 | 23.307339 | 128 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/A/Atestcensus.py | '''
Test the Subject System A on Census dataset to generate the output for the input given as argv arguments.
All the inputs are assumed to be space separated.
'''
from __future__ import division
from random import seed, shuffle
import random
import math
import os
from collections import defaultdict
from sklearn import... | 1,768 | 21.679487 | 128 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/A/AGroup.py | '''
Group discrimination testing for Subject System A
Inputs :
argv[1] : Train file
argv[2] : Sensitive argument
argv[3] : Argument to test discriminationa gainst
For argv[2] and argv[3] : 8 means race and 9 means gender
'''
from __future__ import division
from random import seed, shuffle
import random
... | 6,049 | 24.854701 | 128 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/A/fair_classification/loss_funcs.py | import sys
import os
import numpy as np
import scipy.special
from collections import defaultdict
import traceback
from copy import deepcopy
def _hinge_loss(w, X, y):
yz = y * np.dot(X,w) # y * (x.w)
yz = np.maximum(np.zeros_like(yz), (1-yz)) # hinge function
return sum(yz)
def _logistic_loss(... | 2,268 | 22.884211 | 82 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/A/fair_classification/utils.py | import numpy as np
from random import seed, shuffle
import loss_funcs as lf # our implementation of loss funcs
from scipy.optimize import minimize # for loss func minimization
from multiprocessing import Pool, Process, Queue
from collections import defaultdict
from copy import deepcopy
import matplotlib.pyplot as plt #... | 27,188 | 41.350467 | 357 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/H/Htest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
from sklearn import svm
import itertools
import sys
random.seed(1)
num_test=0
X=[]
Y=[]
i=0
with open(sys.argv[1],... | 747 | 14.914894 | 54 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/H/Hcreditgroup.py | '''
Group discrimination testing for Subject System H for credit dataset
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from ... | 5,206 | 25.840206 | 94 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/H/Hcreditcausal.py | '''
Causal discrimination testing for Subject System H for credit dataset
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from... | 6,103 | 25.889868 | 102 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/H/Hcredittest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
from sklearn import svm
import itertools
import sys
random.seed(1)
num_test=0
X=[]
Y=[]
i=0
with open(sys.argv[1],... | 747 | 14.914894 | 54 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/H/Hgroup.py | '''
Group discrimination testing for Subject System H
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import line... | 5,202 | 25.545918 | 104 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/H/Hcausal.py | '''
Causal discrimination testing for Subject System H
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import lin... | 6,006 | 26.180995 | 112 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/C/Cgender/Cgroup.py | '''
Group discrimination testing for Subject System C
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import line... | 5,039 | 25.526316 | 104 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/C/Cgender/Ctest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import commands
import sys
random.seed(1991)
trainfile = sys.argv[1]
num_test=0
X=[]
Y=[]
i=0
with o... | 720 | 16.166667 | 42 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/C/Cgender/Ccausal.py | '''
Causal discrimination testing for Subject System C
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import line... | 5,925 | 25.936364 | 112 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/C/Ccredit/Ccreditcausal.py | '''
Causal discrimination testing for Subject System C(Credit dataset)
Inputs :
argv[1] : Train file
argv[2] : Sensitive argument
argv[3] : Argument to test discriminationa gainst
For argv[2] and argv[3] : 8 means race and 9 means gender
'''
from __future__ import division
import random
import math
impo... | 5,841 | 26.556604 | 102 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/C/Ccredit/Ctestcredit.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import commands
import sys
max_inp = 50000
printsuite = 1
minInp = 50000
random.seed(1991)
trainfile =... | 821 | 16.869565 | 54 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/C/Ccredit/Ccreditgroup.py | '''
Group discrimination testing for Subject System C(Credit dataset)
Inputs :
argv[1] : Train file
argv[2] : Sensitive argument
argv[3] : Argument to test discriminationa gainst
For argv[2] and argv[3] : 8 means race and 9 means gender
'''
from __future__ import division
import random
import math
impor... | 5,005 | 25.209424 | 94 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/C/Crace/Cgroup.py | '''
Group discrimination testing for Subject System C
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import line... | 5,027 | 25.1875 | 104 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/C/Crace/Ctest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import commands
import sys
random.seed(1991)
trainfile = sys.argv[1]
num_test=0
X=[]
Y=[]
i=0
with o... | 720 | 16.166667 | 42 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/C/Crace/Ccausal.py | '''
Causal discrimination testing for Subject System C
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import lin... | 5,991 | 25.990991 | 112 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/E/Ecreditgroup.py | '''
Group discrimination testing for Subject System E for credit dataset
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from ... | 4,915 | 25.717391 | 94 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/E/Ecausal.py | '''
Causal discrimination testing for Subject System E
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import lin... | 5,919 | 25.428571 | 112 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/E/Ecreditcausal.py | '''
Causal discrimination testing for Subject System E for causla dataset
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from... | 5,911 | 25.995434 | 102 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/E/Etestcredit.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import sys
max_inp = 50000
printsuite=0
minInp =5
random.seed(2)
X=[]
Y=[]
i=0
with open(sys.argv[1... | 692 | 15.902439 | 42 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/E/Egroup.py | '''
Group discrimination testing for Subject System E
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import line... | 4,900 | 25.491892 | 104 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/E/Etest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import sys
max_inp = 50000
printsuite=0
minInp =5
random.seed(2)
sens_arg = int(sys.argv[2])
X=[]
Y... | 720 | 16.166667 | 42 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/G/Gtest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
import itertools
import sys
max_inp = 30000
printout = 0
minInp=30000
random.seed(1997)
X=[]
Y=[]
i=0
with open(sys.argv[1], "r") as ins:
for line in ins:
... | 703 | 16.170732 | 37 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/G/Gcreditcausal.py | '''
Causal discrimination testing for Subject System G for credit dataset
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from... | 6,129 | 26.244444 | 102 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/G/Gcausal.py | '''
Causal discrimination testing for Subject System G
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import lin... | 6,227 | 25.615385 | 112 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/G/Gcreditgroup.py | '''
Group discrimination testing for Subject System G for Credit dataset
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from ... | 5,476 | 25.980296 | 89 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/G/Gcredittest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
import itertools
import sys
max_inp = 30000
printout = 0
minInp=3
random.seed(1997)
X=[]
Y=[]
i=0
with open(sys.argv[1], "r") as ins:
for line in ins:
... | 701 | 15.714286 | 37 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure1/G/Ggroup.py | '''
Group discrimination testing for Subject System G
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import line... | 5,525 | 25.825243 | 99 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 1,111 | 17.533333 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/D/Dgenderm/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 934 | 16.641509 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/D/Dgenderc/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 935 | 16.660377 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/D/Dracemr/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 935 | 16.660377 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/D/Dracem/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 934 | 16.641509 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/A/ACausal.py | '''
Group discrimination testing for Subject System A
Inputs :
argv[1] : Train file
argv[2] : Sensitive argument
argv[3] : Argument to test discriminationa gainst
For argv[2] and argv[3] : 8 means race and 9 means gender
'''
from __future__ import division
from random import seed, shuffle
import random
... | 6,556 | 25.872951 | 127 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/A/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 935 | 16.660377 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/A/Atestcensus.py | '''
Test the Subject System A on Census dataset to generate the output for the input given as argv arguments.
All the inputs are assumed to be space separated.
'''
from __future__ import division
from random import seed, shuffle
import random
import math
import os
from collections import defaultdict
from sklearn import... | 1,768 | 21.679487 | 128 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/A/fair_classification/loss_funcs.py | import sys
import os
import numpy as np
import scipy.special
from collections import defaultdict
import traceback
from copy import deepcopy
def _hinge_loss(w, X, y):
yz = y * np.dot(X,w) # y * (x.w)
yz = np.maximum(np.zeros_like(yz), (1-yz)) # hinge function
return sum(yz)
def _logistic_loss(... | 2,268 | 22.884211 | 82 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/A/fair_classification/utils.py | import numpy as np
from random import seed, shuffle
import loss_funcs as lf # our implementation of loss funcs
from scipy.optimize import minimize # for loss func minimization
from multiprocessing import Pool, Process, Queue
from collections import defaultdict
from copy import deepcopy
import matplotlib.pyplot as plt #... | 27,188 | 41.350467 | 357 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Cgender/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 934 | 16.641509 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Cgender/Ctest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import commands
import sys
random.seed(1991)
trainfile = sys.argv[1]
num_test=0
X=[]
Y=[]
i=0
with o... | 720 | 16.166667 | 42 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Cgender/Ccausalm.py | '''
Causal discrimination testing for Subject System C
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import line... | 5,883 | 25.86758 | 100 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Ccredit/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 935 | 16.660377 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Ccredit/Ccreditcausal.py | '''
Causal discrimination testing for Subject System C(Credit dataset)
Inputs :
argv[1] : Train file
argv[2] : Sensitive argument
argv[3] : Argument to test discriminationa gainst
For argv[2] and argv[3] : 8 means race and 9 means gender
'''
from __future__ import division
import random
import math
impo... | 5,838 | 26.542453 | 104 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Ccredit/Ctestcredit.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import commands
import sys
max_inp = 50000
printsuite = 1
minInp = 50000
random.seed(1991)
trainfile =... | 821 | 16.869565 | 54 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Cracerg/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 935 | 16.660377 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Cracerg/Ctest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import commands
import sys
random.seed(1991)
trainfile = sys.argv[1]
num_test=0
X=[]
Y=[]
i=0
with o... | 720 | 16.166667 | 42 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Cracerg/Ccausal.py | '''
Causal discrimination testing for Subject System C
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import lin... | 6,061 | 25.823009 | 100 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Crace/Ccausala.py | '''
Causal discrimination testing for Subject System C
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import lin... | 5,932 | 25.846154 | 100 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Crace/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 935 | 16.660377 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/C/Crace/Ctest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import commands
import sys
random.seed(1991)
trainfile = sys.argv[1]
num_test=0
X=[]
Y=[]
i=0
with o... | 720 | 16.166667 | 42 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/F/Fcountryrace/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 936 | 16.679245 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/F/Frelationrace/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 935 | 16.660377 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/B/Bracecausal/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 934 | 16.641509 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/B/Bgendercausalm/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 934 | 16.641509 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/B/Bgendercausalmrg/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 935 | 16.660377 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/B/Bgendercausalrg/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 935 | 16.660377 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/E/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 934 | 16.641509 | 108 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/E/Ecausalmarital.py | '''
Causal discrimination testing for Subject System E
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import lin... | 5,866 | 25.191964 | 99 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/E/Etest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import sys
max_inp = 50000
printsuite=0
minInp =5
random.seed(2)
sens_arg = int(sys.argv[2])
X=[]
Y... | 720 | 16.166667 | 42 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/G/Gtest.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
import itertools
import sys
max_inp = 30000
printout = 0
minInp=30000
random.seed(1997)
X=[]
Y=[]
i=0
with open(sys.argv[1], "r") as ins:
for line in ins:
... | 703 | 16.170732 | 37 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/G/Gcausale.py | '''
Causal discrimination testing for Subject System G
Inputs :
argv[1] : Train file
argv[2] : Argument to test discriminationa gainst
8 means race and 9 means gender
'''
from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import lin... | 6,268 | 25.563559 | 99 | py |
Themis | Themis-master/ESEC.FSE.2017.Experimental.Replication/Figure2/G/Table1CausalScore.py | '''
This script calculates the Causal discrimination score for the particular input file towards race or gender.
USAGE :
argv[1] : Input test suite
argv[2] : 0/1
0 for tace
1 for gender
'''
import sys
f = open(sys.argv[1],"r")
type = int(sys.argv[2])
#type = 0 means race
#type ... | 934 | 16.641509 | 108 | py |
Themis | Themis-master/subjectSystems/A/svm.py | from __future__ import division
from random import seed, shuffle
import random
import math
import os
from collections import defaultdict
from sklearn import svm
import os,sys
import urllib2
sys.path.insert(0, './fair_classification/') # the code for fair classification is in this directory
import utils as ut
import num... | 3,863 | 32.894737 | 127 | py |
Themis | Themis-master/subjectSystems/A/A_causal.py | from __future__ import division
from random import seed, shuffle
import random
import math
import os
from collections import defaultdict
from sklearn import svm
import os,sys
import urllib2
sys.path.insert(0, './fair_classification/') # the code for fair classification is in this directory
import utils as ut
import num... | 11,777 | 45.007813 | 133 | py |
Themis | Themis-master/subjectSystems/A/A_group.py | from __future__ import division
from random import seed, shuffle
import random
import math
import os
from collections import defaultdict
from sklearn import svm
import os,sys
import urllib2
sys.path.insert(0, './fair_classification/') # the code for fair classification is in this directory
import utils as ut
import num... | 10,117 | 42.055319 | 127 | py |
Themis | Themis-master/subjectSystems/A/fair_classification/loss_funcs.py | import sys
import os
import numpy as np
import scipy.special
from collections import defaultdict
import traceback
from copy import deepcopy
def _hinge_loss(w, X, y):
yz = y * np.dot(X,w) # y * (x.w)
yz = np.maximum(np.zeros_like(yz), (1-yz)) # hinge function
return sum(yz)
def _logistic_loss(... | 2,268 | 22.884211 | 82 | py |
Themis | Themis-master/subjectSystems/A/fair_classification/utils.py | import numpy as np
from random import seed, shuffle
import loss_funcs as lf # our implementation of loss funcs
from scipy.optimize import minimize # for loss func minimization
from multiprocessing import Pool, Process, Queue
from collections import defaultdict
from copy import deepcopy
import matplotlib.pyplot as plt #... | 27,528 | 41.352308 | 357 | py |
Themis | Themis-master/subjectSystems/fairness_unaware/nb_group.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import sys
max_inp = 1000
random.seed()
X=[]
Y=[]
i=0
with open("cleaned_train", "r") as ins:
f... | 9,066 | 47.228723 | 95 | py |
Themis | Themis-master/subjectSystems/fairness_unaware/nb_causal.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
import itertools
import sys
max_inp = 1000
random.seed()
num_test=0
X=[]
Y=[]
i=0
with open("cleaned_train", "r") a... | 10,452 | 47.845794 | 133 | py |
Themis | Themis-master/subjectSystems/fairness_unaware/svm_group.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
from sklearn import svm
import itertools
import sys
max_inp = 1000
random.seed()
X=[]
Y=[]
i=0
with open("cleaned_tr... | 9,067 | 46.726316 | 95 | py |
Themis | Themis-master/subjectSystems/fairness_unaware/lr_group.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
import itertools
import sys
max_inp = 1000
random.seed()
X=[]
Y=[]
i=0
with open("cleaned_train", "r") as ins:
for line in ins:
line = line.strip()
... | 3,976 | 25.691275 | 81 | py |
Themis | Themis-master/subjectSystems/fairness_unaware/lr_causal.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
import itertools
import sys
max_inp = 1000
random.seed()
num_test=0
X=[]
Y=[]
i=0
with open("cleaned_train", "r") as ins:
for line in ins:
line = l... | 4,436 | 24.796512 | 85 | py |
Themis | Themis-master/subjectSystems/fairness_unaware/svm_causal.py | from __future__ import division
import random
import math
import os
from collections import defaultdict
from sklearn import linear_model
import numpy as np
from sklearn.naive_bayes import GaussianNB
from sklearn import svm
import itertools
import sys
max_inp = 1000
random.seed()
num_test=0
X=[]
Y=[]
i=0
with open... | 10,548 | 47.837963 | 133 | py |
Themis | Themis-master/Themis2.0/themis2.py | # Themis 2.0
#
# By: Rico Angell
from __future__ import division
import argparse
import subprocess
from itertools import chain, combinations, product
import math
import random
import scipy.stats as st
import xml.etree.ElementTree as ET
import copy
class Input:
"""
Class to define an input characteristic to ... | 23,477 | 34.626707 | 139 | py |
Themis | Themis-master/Themis2.0/software.py | import sys
sex = sys.argv[1]
race = sys.argv[3]
if(sex=="Male" and race=="Red"):
print "1"
else:
print "0"
| 116 | 12 | 32 | py |
Themis | Themis-master/Themis2.0/grid2.py | import sys
from PyQt5.QtWidgets import *
import PyQt5.QtGui as QtGui
from PyQt5.QtGui import *
from PyQt5.QtCore import *
import xml.etree.ElementTree as ET
import themis2
tree = None
class App(QDialog):
def __init__(self):
super().__init__()
self.title = 'Themis 2.0'
self.left = 500
... | 31,234 | 35.617819 | 164 | py |
Themis | Themis-master/Themis2.0/loan.py | import sys
sex = sys.argv[1]
race = sys.argv[2]
income = sys.argv[3]
# first case
if sex == "male":
print ("1")
elif race != "green":
if income == "0...50000":
print ("0")
else:
print ("1")
else:
if income == "0...50000" or income == "50001...100000":
print ("0")
else:
print ("1")
| 299 | 12.636364 | 56 | py |
Themis | Themis-master/Themis2.0/loan_2.py | import sys
sex = sys.argv[1]
race = sys.argv[2]
income = sys.argv[3]
# second case
if race == "green" or race == "orange":
if income == "0...50000":
print ("0")
else:
print ("1")
else:
if income == "50001...100000":
print ("0")
else:
print ("1")
| 260 | 13.5 | 39 | py |
Themis | Themis-master/Themis2.0/grid.py | import sys
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
import xml.etree.ElementTree as ET
import themis2
class App(QDialog):
def __init__(self):
super().__init__()
self.title = 'Themis 2.0'
self.left = 100
self.top = 100
self.width ... | 10,797 | 28.746556 | 97 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.