repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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fairness-comparison | fairness-comparison-master/fairness/algorithms/baseline/Generic.py | from fairness.algorithms.Algorithm import Algorithm
class Generic(Algorithm):
def __init__(self):
Algorithm.__init__(self)
## self.classifier should be set in any class that extends this one
def run(self, train_df, test_df, class_attr, positive_class_val, sensitive_attrs,
single_se... | 1,209 | 34.588235 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/baseline/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/ZafarAlgorithm.py | from fairness.algorithms.Algorithm import Algorithm
import numpy
import tempfile
import os
import subprocess
import json
import sys
import numpy
class ZafarAlgorithmBase(Algorithm):
def __init__(self):
Algorithm.__init__(self)
def get_supported_data_types(self):
return set(["numerical-binsens... | 4,327 | 33.07874 | 92 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_mistreatment/synthetic_data_demo/decision_boundary_demo.py | import os,sys
import numpy as np
from generate_synthetic_data import *
sys.path.insert(0, '../../fair_classification/') # the code for fair classification is in this directory
import utils as ut
import funcs_disp_mist as fdm
import plot_syn_boundaries as psb
def test_synthetic_data():
""" Generate the synthetic d... | 3,829 | 43.022989 | 217 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_mistreatment/synthetic_data_demo/fairness_acc_tradeoff.py | import os,sys
import numpy as np
from generate_synthetic_data import *
sys.path.insert(0, '../../fair_classification/') # the code for fair classification is in this directory
import utils as ut
import funcs_disp_mist as fdm
import loss_funcs as lf # loss funcs that can be optimized subject to various constraints
impor... | 4,031 | 34.368421 | 206 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_mistreatment/synthetic_data_demo/generate_synthetic_data.py | from __future__ import division
import os,sys
import math
import numpy as np
import matplotlib.pyplot as plt # for plotting stuff
from random import seed, shuffle
from scipy.stats import multivariate_normal # generating synthetic data
from sklearn.linear_model import LogisticRegression
SEED = 1122334455
seed(SEED) # se... | 5,351 | 37.782609 | 168 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_mistreatment/synthetic_data_demo/plot_syn_boundaries.py | import matplotlib
import matplotlib.pyplot as plt # for plotting stuff
import os
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams.update({'figure.autolayout': True})
def get_line_coordinates(w, x1, x2):
y1 = (-w[0] - (w[1] * x1)) / w[2]
y2 = (-w[0] - (w[1] * x2)) / w[2]
return y1,y2
def p... | 2,047 | 32.032258 | 168 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_mistreatment/propublica_compas_data_demo/load_compas_data.py | from __future__ import division
import urllib2
import os,sys
import numpy as np
import pandas as pd
from collections import defaultdict
from sklearn import feature_extraction
from sklearn import preprocessing
from random import seed, shuffle
sys.path.insert(0, '../../fair_classification/') # the code for fair classifi... | 5,524 | 33.968354 | 204 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_mistreatment/propublica_compas_data_demo/demo_constraints.py | import os,sys
import numpy as np
from load_compas_data import *
sys.path.insert(0, '../../fair_classification/') # the code for fair classification is in this directory
import utils as ut
import funcs_disp_mist as fdm
def test_compas_data():
""" Generate the synthetic data """
data_type = 1
X, y, x_control = l... | 2,327 | 33.235294 | 206 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_impact/adult_data_demo/prepare_adult_data.py | 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 numpy as np
from random import seed, shuffle
SEED = 1122334455
seed(SEED) # set the random seed so that the random permutations can be reproduced again
np.rando... | 6,476 | 38.981481 | 295 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_impact/adult_data_demo/demo_constraints.py | import os,sys
import numpy as np
from prepare_adult_data import *
sys.path.insert(0, '../../fair_classification/') # the code for fair classification is in this directory
import utils as ut
import loss_funcs as lf # loss funcs that can be optimized subject to various constraints
def test_adult_data():
""" Load t... | 4,261 | 44.340426 | 196 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_impact/run-classifier/main.py | import os,sys
import numpy as np
sys.path.insert(0, '../../fair_classification/') # the code for fair classification is in this directory
import utils as ut
import loss_funcs as lf # loss funcs that can be optimized subject to various constraints
import json
def train_classifier(x, y, control, sensitive_attrs, mode, s... | 4,660 | 39.530435 | 169 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_impact/run-classifier/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 |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_impact/run-classifier/prepare_adult_data.py | 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 numpy as np
from random import seed, shuffle
SEED = 1122334455
seed(SEED) # set the random seed so that the random permutations can be reproduced again
np.rando... | 6,477 | 38.742331 | 295 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_impact/run-classifier/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 sys
SEED = 1122334455
see... | 27,428 | 41.198462 | 357 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_impact/synthetic_data_demo/decision_boundary_demo.py | import os,sys
import numpy as np
from generate_synthetic_data import *
sys.path.insert(0, '../../fair_classification/') # the code for fair classification is in this directory
import utils as ut
import loss_funcs as lf # loss funcs that can be optimized subject to various constraints
def test_synthetic_data():
""... | 6,067 | 46.40625 | 196 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_impact/synthetic_data_demo/fairness_acc_tradeoff.py | import os,sys
import numpy as np
from generate_synthetic_data import *
sys.path.insert(0, '../../fair_classification/') # the code for fair classification is in this directory
import utils as ut
import loss_funcs as lf # loss funcs that can be optimized subject to various constraints
NUM_FOLDS = 10 # we will show 10-f... | 2,207 | 45.978723 | 331 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/disparate_impact/synthetic_data_demo/generate_synthetic_data.py | import math
import numpy as np
import matplotlib.pyplot as plt # for plotting stuff
from random import seed, shuffle
from scipy.stats import multivariate_normal # generating synthetic data
SEED = 1122334455
seed(SEED) # set the random seed so that the random permutations can be reproduced again
np.random.seed(SEED)
de... | 4,038 | 40.214286 | 168 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/preferential_fairness/adult_data_demo/prepare_adult_data.py | import os,sys
import urllib2
import numpy as np
from random import seed, shuffle
from sklearn import preprocessing
import pickle
SEED = 1122
seed(SEED) # set the random seed so that the random permutations can be reproduced again
np.random.seed(SEED)
"""
The adult dataset can be obtained from: http://archive.ics.... | 6,578 | 37.028902 | 287 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/preferential_fairness/adult_data_demo/demo_constraints.py | from __future__ import division
import os,sys
import numpy as np
from prepare_adult_data import load_adult_data
from sklearn.model_selection import train_test_split
sys.path.insert(0, '../../fair_classification/') # the code for fair classification is in this directory
import stats_pref_fairness as compute_stats
from... | 4,300 | 36.72807 | 229 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/preferential_fairness/synthetic_data_demo/decision_boundary_demo.py | from __future__ import division
import os,sys
import numpy as np
from generate_synthetic_data import *
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt # for plotting stuff
sys.path.insert(0, '../../fair_classification/') # the code for fair classification is in this directory
from p... | 5,663 | 40.647059 | 229 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/preferential_fairness/synthetic_data_demo/plot_synthetic_boundaries.py | import matplotlib
import matplotlib.pyplot as plt # for plotting stuff
import os
import numpy as np
matplotlib.rcParams['text.usetex'] = True # for type-1 fonts
def get_line_coordinates(w, x1, x2):
y1 = (-w[0] - (w[1] * x1)) / w[2]
y2 = (-w[0] - (w[1] * x2)) / w[2]
return y1,y2
def plot_data(X, y, x_... | 3,067 | 38.844156 | 158 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/preferential_fairness/synthetic_data_demo/generate_synthetic_data.py | import sys
import math
import numpy as np
from random import seed, shuffle
from scipy.stats import multivariate_normal # generating synthetic data
def generate_synthetic_data(data_type, n_samples):
"""
Code for generating the synthetic data.
We will have two non-sensitive features and one sensitiv... | 2,805 | 31.252874 | 168 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/fair_classification/funcs_disp_mist.py | from __future__ import division
import os,sys
import traceback
import numpy as np
from random import seed, shuffle
from collections import defaultdict
from copy import deepcopy
from cvxpy import *
import dccp
from dccp.problem import is_dccp
import utils as ut
SEED = 1122334455
seed(SEED) # set the random seed so that... | 21,343 | 44.029536 | 357 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/fair_classification/stats_pref_fairness.py | from __future__ import division
import numpy as np
from sklearn.preprocessing import MaxAbsScaler # normalize data with 0 and 1 as min/max absolute vals
import scipy
from multiprocessing import Pool, Process, Queue
from sklearn.metrics import roc_auc_score
import traceback
def get_acc_all(dist_arr, y):
"""
Ge... | 5,735 | 27.256158 | 136 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/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 |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/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 sys
SEED = 1122334455
see... | 27,428 | 41.198462 | 357 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/zafar/fair-classification-master/fair_classification/linear_clf_pref_fairness.py | from __future__ import division
import os,sys
import numpy as np
import traceback
sys.path.insert(0, "/home/mzafar/libraries/dccp") # we will store the latest version of DCCP here.
from cvxpy import *
import dccp
from dccp.problem import is_dccp
class LinearClf():
def __init__(self, loss_function, lam=None... | 11,846 | 33.438953 | 268 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/blandin/BlandinAlgorithm.py | from BlackBoxAuditing.repairers.GeneralRepairer import Repairer
from pandas import DataFrame
from fairness.algorithms.Algorithm import Algorithm
from fairness.algorithms.baseline.Generic import Generic
REPAIR_LEVEL_DEFAULT = 1.0
class BlandinAlgorithm(Generic):
def __init__(self, model):
Generic.__init__(... | 419 | 31.307692 | 63 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/blandin/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/algorithms/feldman/FeldmanAlgorithm.py | from BlackBoxAuditing.repairers.GeneralRepairer import Repairer
from pandas import DataFrame
from fairness.algorithms.Algorithm import Algorithm
REPAIR_LEVEL_DEFAULT = 1.0
class FeldmanAlgorithm(Algorithm):
def __init__(self, algorithm):
Algorithm.__init__(self)
self.model = algorithm
self... | 2,515 | 38.936508 | 98 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/feldman/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/svm.py | import math
import numpy
from numpy.linalg import norm
import random
from fairness.algorithms.Ben import utils
#from utils import sign
DEFAULT_NUM_ROUNDS = 1
DEFAULT_LAMBDA = 1.0
DEFAULT_GAMMA = 0.1
def hyperplaneToHypothesis(w):
return lambda x: sign(numpy.dot(w,x))
# use scikit-learn to do the svm for us
def ... | 1,703 | 27.4 | 78 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/errorfunctions.py | from fairness.algorithms.Ben import utils
import random
import heapq
def minLabelErrorOfHypothesisAndNegation(data, h):
posData, negData = ([(x, y) for (x, y) in data if h(x) == 1],
[(x, y) for (x, y) in data if h(x) == 0])
posError = sum(y == 0 for (x, y) in posData) + sum(y == 1 for (... | 3,951 | 37 | 107 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/margin.py | from fairness.algorithms.Ben.utils import *
from fairness.algorithms.Ben.errorfunctions import *
from fairness.algorithms.Ben import svm
import numpy
from fairness.algorithms.Ben import lr
from fairness.algorithms.Ben import boosting
from fairness.algorithms.Ben.weaklearners.decisionstump import buildDecisionStump
imp... | 7,406 | 39.47541 | 119 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/utils.py | import random
import math
import numpy
# draw: [float] -> int
# pick an index from the given list of floats proportionally
# to the size of the entry (i.e. normalize to a probability
# distribution and draw according to the probabilities).
def draw(weights):
choice = random.uniform(0, sum(weights))
choiceIndex =... | 4,214 | 26.37013 | 116 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/boosting.py | import math
from fairness.algorithms.Ben.utils import *
from fairness.algorithms.Ben.errorfunctions import labelError
from fairness.algorithms.Ben.weaklearners.decisionstump import buildDecisionStump
# compute the weighted error of a given hypothesis on a distribution
# return all of the hypothesis results and the err... | 3,053 | 38.153846 | 128 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/SDBSVM.py | #!/usr/bin/env python3
import random
from collections import OrderedDict
from fairness.algorithms.Ben import boosting
from fairness.algorithms.Ben import svm
from fairness.algorithms.Ben import lr
from fairness.data.objects import Adult, German
from fairness.algorithms.Ben.weaklearners.decisionstump import buildDecis... | 4,979 | 39.16129 | 113 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/lr.py | import random
import numpy
from decimal import Decimal
from fairness.algorithms.Ben import utils
#from utils import sigmoid, sign, zeroOneSign
def lrDetailedSKL(data):
from sklearn import linear_model
points, labels = zip(*data)
clf = linear_model.LogisticRegression()
lrClassifier = clf.fit(points, labels)... | 554 | 29.833333 | 119 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/SDBAdaBoost.py | #!/usr/bin/env python3
import random
from fairness.algorithms.Ben import boosting
from fairness.algorithms.Ben import svm
from fairness.algorithms.Ben import lr
from fairness.data.objects import Adult, German
from fairness.algorithms.Ben.weaklearners.decisionstump import buildDecisionStump
from fairness.algorithms.Be... | 3,348 | 39.349398 | 122 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/weaklearners/nearestneighbor.py | def dist(x,y):
return sum((a-b)**2 for (a,b) in zip(x,y))
def nearestLearner(draw):
data = [draw() for _ in range(100)]
def classify(x):
return min(data, key=lambda y: dist(x, y[0]))[1]
return classify
| 224 | 17.75 | 54 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/weaklearners/decisiontree.py | import math
class Tree:
def __init__(self, parent=None):
self.parent = parent
self.leftChild = None
self.rightChild = None
self.label = None
self.classCounts = None
self.splitThreshold = None
self.splitFeature = None
def dataToDistribution(data):
''' Turn a dataset whi... | 3,270 | 27.692982 | 93 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/weaklearners/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/algorithms/Ben/weaklearners/decisionstump.py | from fairness.algorithms.Ben.errorfunctions import minLabelErrorOfHypothesisAndNegation
import sys
class Stump:
def __init__(self):
self.gtLabel = None
self.ltLabel = None
self.splitThreshold = None
self.splitFeature = None
def classify(self, point):
if point[self.splitFeature] >=... | 2,280 | 32.057971 | 131 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/CaldersAlgorithm.py | from fairness.algorithms.Algorithm import Algorithm
import numpy
import tempfile
import os
import subprocess
class CaldersAlgorithm(Algorithm):
"""
Notes:
- The original code depends on python2's commands library. We hacked
it to have python3 support by adding a minimal commands.py module with
a g... | 5,092 | 34.866197 | 98 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/KamishimaAlgorithm.py | from fairness.algorithms.Algorithm import Algorithm
import numpy
import tempfile
import os
import subprocess
class KamishimaAlgorithm(Algorithm):
"""
Notes:
- The original code depends on python2's commands library. We hacked
it to hve python3 support by adding a minimal commands.py module with
a ... | 4,509 | 35.08 | 99 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/predict_nb.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Predict classes for naive Bayes model
SYNOPSIS::
SCRIPT [options]
Description
===========
Columns of Outputs:
1. true sample class number
2. predicted class number
3. sensitive feature
4. class 0 probability
5. class 1 probability
Delimiters of columns are a s... | 8,473 | 29.37276 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/train_nb.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Training naive Bayes
SYNOPSIS::
SCRIPT [options]
Description
===========
The last column indicates binary class.
Options
=======
-i <INPUT>, --in <INPUT>
specify <INPUT> file name
-o <OUTPUT>, --out <OUTPUT>
specify <OUTPUT> file name
-b <BETA>, --beta... | 8,102 | 29.693182 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/train_lr.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
training logistic regression
SYNOPSIS::
SCRIPT [options]
Description
===========
The last column indicates binary class.
Options
=======
-i <INPUT>, --in <INPUT>
specify <INPUT> file name
-o <OUTPUT>, --out <OUTPUT>
specify <OUTPUT> file name
--ns
... | 7,731 | 29.321569 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fai_bin_bin.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Compute various types of fairness-aware indexes.
SYNOPSIS::
SCRIPT [options] [<INPUT> [<OUTPUT>]]
Description
===========
Input
-----
Both a class and a sensitive attribute are assumed to be binary. As default,
the first, the second, and the third columns indic... | 8,071 | 29.575758 | 80 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/commands.py | # minimal polyfill for commands in python3
import subprocess
def getoutput(cmd):
output = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE).stdout
output = str(output, 'utf-8')
return output
| 212 | 20.3 | 75 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/train_cv2nb.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Training Calders and Verwer's two naive Bayes.
SYNOPSIS::
SCRIPT [options]
Description
===========
The last column indicates binary class.
Options
=======
-i <INPUT>, --in <INPUT>
specify <INPUT> file name
-o <OUTPUT>, --out <OUTPUT>
specify <OUTPUT> f... | 7,847 | 29.776471 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/predict_lr.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Predict classes for logistic regression model
SYNOPSIS::
SCRIPT [options]
Description
===========
Columns of Outputs:
1. true sample class number
2. predicted class number
3. sensitive feature
4. class 0 probability
5. class 1 probability
Delimiters of columns... | 8,573 | 29.731183 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/train_pr.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
training logistic regression
SYNOPSIS::
SCRIPT [options]
Description
===========
The last column indicates binary class.
Options
=======
-i <INPUT>, --in <INPUT>
specify <INPUT> file name
-o <OUTPUT>, --out <OUTPUT>
specify <OUTPUT> file name
-C <REG>,... | 9,879 | 29.875 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/creditg_p_data.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
convert credit-g.data => creditg_p.data
"""
import sys
for line in sys.stdin.readlines():
line = line.rstrip('\r\n')
f = line.split(" ")
sys.stdout.write(" ".join(f[0:8]) + " ")
sys.stdout.write(" ".join(f[9:20]) + " ")
if f[8] == "1":
sys... | 431 | 19.571429 | 48 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/select_sensitive.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Move the specified features to the last position
SYNOPSIS::
SCRIPT [options]
Options
=======
-i <INPUT>, --in <INPUT>
specify <INPUT> file name
-o <OUTPUT>, --out <OUTPUT>
specify <OUTPUT> file name
-f <FEATURE>, --feature <FEATURE>
the feature numbe... | 5,738 | 30.883333 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/sdata_cv.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Generate Artificial Data Set for Discrimination/Fairness-aware learning
SYNOPSIS::
SCRIPT [options]
Description
===========
.. math::
\Pr[C, L, P, A_1, \cdots, A_f] =
\Pr[L] \Pr[A] \Pr[C | L, A] \Pr[A_1 | L, S] \cdots \Pr[A_f | L, S]
Features :mat... | 12,083 | 32.660167 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/sdata_zkc1.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Synthetic Data Generator
Zliobaite+ "Handling Conditional Discrimination" Example 2 in Table III
* Y : Acceptance, rejected=0, accepted=1
* S : Gender, 0=Female, 1=Male
* P : Program, 0=medicine, 1=Computer Science
* T : Test Score, 1..100
Output Format::
T<sp>P<s... | 1,253 | 25.125 | 71 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/add_quad_terms.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Add quadratic terms to tab/comma/space separated data
SYNOPSIS::
SCRIPT [options] [<INPUT> [<OUTPUT>]]
Description
-----------
Quadratic terms of specified variables are genereted, and these are inserted
just before the last <LAST> columns.
Options
-------
-i <I... | 6,712 | 29.513636 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/adult_discritize.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Discretize *adult* data
SYNOPSIS::
SCRIPT [options]
Description
===========
Discretize *adult* data as the procedure written in [DMKD2010]_.
- Integer attributes are divided into 4 bins each of which contains
equal numbers of samples.
- Nominal attribute valu... | 7,937 | 27.553957 | 83 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/creditg_j_bindata.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
convert credit-g.data => creditg_p.data
"""
import sys
def write_data_with_binary(a_list, d_list):
# for each attribute
for a, d in zip(a_list, d_list):
# numeric attribute?
if a <= 2:
sys.stdout.write(d)
else:
... | 835 | 21.594595 | 67 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/sdata_zkc2.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Synthetic Data Generator
Zliobaite+ "Handling Conditional Discrimination" Example 2 in Table III
* Y : Acceptance, rejected=0, accepted=1
* S : Gender, 0=Female, 1=Male
* P : Program, 0=medicine, 1=Computer Science
* T : Test Score, 1..100
Output Format::
T<sp>P<s... | 1,253 | 25.125 | 71 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/creditg_f_bindata.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
convert credit-g.data => creditg_p.data
"""
import sys
def write_data_with_binary(a_list, d_list):
# for each attribute
for a, d in zip(a_list, d_list):
# numeric attribute?
if a <= 2:
sys.stdout.write(d)
else:
... | 786 | 20.861111 | 67 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/creditg_j_data.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
convert credit-g.data => creditg_p.data
"""
import sys
for line in sys.stdin.readlines():
line = line.rstrip('\r\n')
f = line.split(" ")
sys.stdout.write(" ".join(f[0:16]) + " ")
sys.stdout.write(" ".join(f[17:20]) + " ")
if f[16] == "0":
... | 434 | 19.714286 | 48 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/sdata_kam1.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Kamishima's synthetic data generator
(e1, e2) \sim Normal([0,0], [[1, rho], [rho, 1]])
X1 = 1 + e1
X2 = 1 + e1, if s==1; -1 + e1, if s==0
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import numpy as ... | 1,143 | 24.422222 | 77 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/creditg_f_data.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
convert credit-g.data => creditg_p.data
"""
import sys
for line in sys.stdin.readlines():
line = line.rstrip('\r\n')
f = line.split(" ")
sys.stdout.write(" ".join(f[0:19]) + " ")
if f[19] == "0":
sys.stdout.write("0 ")
else:
sys.st... | 387 | 18.4 | 48 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/creditg_p_bindata.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
convert credit-g.data => creditg_p.data
"""
import sys
def write_data_with_binary(a_list, d_list):
# for each attribute
for a, d in zip(a_list, d_list):
# numeric attribute?
if a <= 2:
sys.stdout.write(d)
else:
... | 830 | 21.459459 | 67 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/data/adult_arff.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Convert *adult.data* or *adult.test* to ARFF format
adult data set (a.k.a. census income data set)
http://archive.ics.uci.edu/ml/datasets/Adult
SYNOPSIS::
SCRIPT [options]
Options
=======
-i <INPUT>, --in <INPUT>
specify <INPUT> file name
-o <OUTPUT>, --out... | 6,039 | 32.932584 | 442 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/__init__.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
FADM: Fairness-aware Data Mining
"""
#==============================================================================
# Module metadata variables
#==============================================================================
__author__ = "Toshihiro Kamishima ( http://... | 2,511 | 32.052632 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/eval/_bin_class_bin_sensitive.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Compute various types of fairness-aware indexes.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
#==============================================================================
# Module metadata variabl... | 16,393 | 28.275 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/eval/_bin_class.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
import from 50b745c1d18d5c4b01d9d00e406b5fdaab3515ea @ KamLearn
Compute various statistics between estimated and correct classes in binary
cases
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
#=======... | 16,079 | 26.772021 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/eval/__init__.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Evaluation Metrics
"""
#==============================================================================
# Imports
#==============================================================================
from ._bin_class import *
from ._bin_class_bin_sensitive import * | 310 | 24.916667 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/util/_base.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
import from 50b745c1d18d5c4b01d9d00e406b5fdaab3515ea @ KamLearn
Utility routines
"""
#==============================================================================
# Module metadata variables
#==========================================================================... | 4,058 | 25.880795 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/util/__init__.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Utilities
"""
#==============================================================================
# Imports
#==============================================================================
from ._base import *
| 257 | 20.5 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/lr/pr.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Two Class logistic regression module with Prejudice Remover
the number of sensitive features is restricted to one, and the feature must
be binary.
Attributes
----------
EPSILON : floast
small positive constant
N_S : int
the number of sensitive features
N_CLASS... | 15,688 | 30.128968 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/lr/__init__.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Logistic Regression
"""
#==============================================================================
# Imports
#==============================================================================
#=========================================================================... | 441 | 26.625 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/nb/_nb.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
import from 50b745c1d18d5c4b01d9d00e406b5fdaab3515ea @ KamLearn
naive Bayes classifier that can update incrementally
scikit-learn compatible interface
"""
#==============================================================================
# Module metadata variables
#===... | 21,737 | 31.204444 | 88 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/nb/__init__.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Naive Bayes
"""
#==============================================================================
# Imports
#==============================================================================
from ._nb import *
#=============================================================... | 456 | 24.388889 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/nb/cv2nb.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Calders and Verwer's two naive Bayes method
.. [DMKD2010] T.Calders and S.Verwer "Three naive Bayes approaches for
discrimination-free classification" Data Mining and Knowledge Discovery,
vol.21 (2010)
"""
from __future__ import print_function
from __future__ ... | 7,977 | 32.241667 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/algorithms/kamishima/kamfadm-2012ecmlpkdd/fadm/nb/tests/test_cv2nb.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from numpy.testing import assert_array_equal, assert_array_almost_equal
import unittest
##### Test Classes #####
class TestCaldersVerwerTwoNaiveBayes(unittest.T... | 895 | 29.896552 | 71 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/EqOppo_fp_ratio.py | """ Equal opportunity - Protected and unprotected False postives ratio"""
import math
import sys
import numpy as np
from fairness.metrics.utils import calc_fp_fn
from fairness.metrics.Metric import Metric
class EqOppo_fp_ratio(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'EqOppo_... | 917 | 34.307692 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/CalibrationPos.py | from fairness.metrics.Metric import Metric
class CalibrationPos(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'calibration+'
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitive_name,
unprotected_vals, positive_pred, dict_of_nonclass_... | 821 | 36.363636 | 86 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/CalibrationNeg.py | from fairness.metrics.Metric import Metric
class CalibrationNeg(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'calibration-'
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitive_name,
unprotected_vals, positive_pred, dict_of_nonclass_... | 821 | 36.363636 | 86 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/Average.py | from fairness.metrics.Metric import Metric
class Average(Metric):
"""
Takes the average (mean) of a given list of metrics. Assumes that if the total over all
metrics is 0, the returned result should be 1.
"""
def __init__(self, metrics_list, name):
Metric.__init__(self)
se... | 1,097 | 34.419355 | 93 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/ExpWelf.py | import sys
import numpy as np
import math
from fairness.metrics.utils import calc_pos_protected_percents
from fairness.metrics.UtilityMetric import UtilityMetric
class ExpWelf(UtilityMetric):
def __init__(self, welfare_fn, cost_fn):
UtilityMetric.__init__(self, welfare_fn, cost_fn)
self.name = f'... | 1,205 | 37.903226 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/FNR.py | from fairness.metrics.Metric import Metric
from fairness.metrics.TPR import TPR
class FNR(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'FNR'
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitive_name,
unprotected_vals, positive_pred, dict_of... | 563 | 36.6 | 93 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/DIAvgAll.py | import math
from fairness.metrics.utils import calc_prob_class_given_sensitive
from fairness.metrics.Metric import Metric
class DIAvgAll(Metric):
"""
This metric calculates disparate imapct in the sense of the 80% rule before the 80%
threshold is applied. This is described as DI in: https://arxiv.org/abs... | 2,054 | 38.519231 | 98 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/SensitiveMetric.py | from fairness.metrics.Average import Average
from fairness.metrics.Diff import Diff
from fairness.metrics.FilterSensitive import FilterSensitive
from fairness.metrics.Metric import Metric
from fairness.metrics.Ratio import Ratio
class SensitiveMetric(Metric):
"""
Takes the given metric and creates a version ... | 5,224 | 54 | 101 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/Metric.py | class Metric:
def __init__(self):
self.name = 'Name not implemented' ## This should be replaced in implemented metrics.
self.iter_counter = 0
def __iter__(self):
self.iter_counter = 0
return self
def __next__(self):
self.iter_counter += 1
if self.iter_count... | 2,644 | 43.083333 | 104 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/ExpCost.py | import sys
import numpy as np
import math
from fairness.metrics.utils import calc_pos_protected_percents
from fairness.metrics.UtilityMetric import UtilityMetric
class ExpCost(UtilityMetric):
def __init__(self, welfare_fn, cost_fn):
UtilityMetric.__init__(self, welfare_fn, cost_fn)
self.name = f'... | 1,178 | 37.032258 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/TPR.py | from fairness.metrics.Metric import Metric
from sklearn.metrics import recall_score
class TPR(Metric):
"""
Returns the true positive rate (aka recall) for the predictions. Assumes binary
classification.
"""
def __init__(self):
Metric.__init__(self)
self.name = 'TPR'
def calc(s... | 554 | 31.647059 | 89 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/FilterSensitive.py | from fairness.metrics.Metric import Metric
class FilterSensitive(Metric):
def __init__(self, metric):
Metric.__init__(self)
self.metric = metric
self.name = metric.get_name()
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitive_name,
unprote... | 1,993 | 42.347826 | 101 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/utils.py |
def calc_pos_protected_percents(predicted, sensitive, unprotected_vals, positive_pred):
"""
Returns P(C=YES|sensitive=privileged) and P(C=YES|sensitive=not privileged) in that order where
C is the predicited classification and where all not privileged values are considered
equivalent. Assumes that pre... | 4,282 | 39.40566 | 99 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/EqOppo_fp_diff.py | """ Equal opportunity - Protected and unprotected False postives difference """
import math
import sys
import numpy
from fairness.metrics.utils import calc_fp_fn
from fairness.metrics.Metric import Metric
class EqOppo_fp_diff(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'EqOppo_f... | 794 | 33.565217 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/BCR.py | from fairness.metrics.Metric import Metric
from fairness.metrics.TNR import TNR
from fairness.metrics.TPR import TPR
class BCR(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'BCR'
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitive_name,
unp... | 830 | 40.55 | 93 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/CVWelf.py | import sys
import numpy as np
import math
from fairness.metrics.UtilityMetric import UtilityMetric
class CVWelf(UtilityMetric):
def __init__(self, welfare_fn, cost_fn, tau=None, transform_welf=None, name=None):
UtilityMetric.__init__(self, welfare_fn, cost_fn)
self.tau = tau
self.transfor... | 1,817 | 36.875 | 86 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/TNR.py | from fairness.metrics.Metric import Metric
from sklearn.metrics import confusion_matrix
class TNR(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'TNR'
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitive_name,
unprotected_vals, positive_pred,... | 996 | 32.233333 | 97 | py |
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