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h205c committed Dec 21, 2017
1 parent ac0bfe6 commit 1aa10dd
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11 changes: 5 additions & 6 deletions algorithms/AbstractAlgorithm.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,12 @@
from abc import ABCMeta, abstractmethod
import os, sys
from preprocessing.prepare_german import prepare_german
from preprocessing.prepare_adult import prepare_adult
from preprocessing.prepare_retailer import prepare_retailer
from preprocessing.prepare_small_retailer import prepare_small_retailer
from preprocessing.prepare_ricci import prepare_ricci
from preprocessing.deprecated.prepare_german import prepare_german
from preprocessing.deprecated.prepare_adult import prepare_adult
from preprocessing.deprecated.prepare_retailer import prepare_retailer
from preprocessing.deprecated.prepare_ricci import prepare_ricci
import numpy as np
import pandas as pd
from preprocessing.black_box_auditing import *
from preprocessing.deprecated.black_box_auditing import *
from data.german.load_german_data import *
from data.adult.load_adult import *
from data.ricci.load_data import *
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66 changes: 58 additions & 8 deletions algorithms/kamishima/KamishimaAlgorithm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,7 +130,7 @@ def CV_score(predicted, protected, total_sensitive, total_nonsensitive):
else:
print("Invalid class value in y_control_test")

def getScore(fixed_y_test,y_classified_results, var=1): #var = 1-acc/DI, 2-acc, 3-DI
def getScore(fixed_y_test,y_classified_results, var=1): #var = |1-DI|/acc, 2-acc, 3-|1-DI|
total_sensitive = 0
total_nonsensitive = 0
for x in self.x_control_test[self.sensitive_attr]:
Expand All @@ -142,17 +142,55 @@ def getScore(fixed_y_test,y_classified_results, var=1): #var = 1-acc/DI, 2-acc,
if var == 2:
return accuracy
elif var == 1:
return abs(1 - accuracy/DI_score(y_classified_results, self.x_control_test[self.sensitive_attr], total_sensitive, total_nonsensitive))
return abs(1 - DI_score(y_classified_results, self.x_control_test[self.sensitive_attr], total_sensitive, total_nonsensitive))/accuracy
else:
return DI_score(y_classified_results, self.x_control_test[self.sensitive_attr], total_sensitive, total_nonsensitive)
return abs(1-DI_score(y_classified_results, self.x_control_test[self.sensitive_attr], total_sensitive, total_nonsensitive))

def binMinMax(first, last, var): #var = |1-DI|/acc, 2-acc, 3-|1-DI|
if first == last:
print(first)
y = train_classify(self.sensitive_attr, self.name, x_train_with_sensitive_feature, self.y_train, x_test_with_sensitive_feature, self.y_test, 1, first, self.x_control_test)
return y
elif (last-1) == first:
firstScore = getScore(fixed_y_test, train_classify(self.sensitive_attr, self.name, x_train_with_sensitive_feature, self.y_train, x_test_with_sensitive_feature, self.y_test, 1, first, self.x_control_test),var)
lastScore = getScore(fixed_y_test, train_classify(self.sensitive_attr, self.name, x_train_with_sensitive_feature, self.y_train, x_test_with_sensitive_feature, self.y_test, 1, last, self.x_control_test),var)
if firstScore <= lastScore:
if var == 1 or var == 3:
return binMinMax(first,first,var)
else:
return binMinMax(last,last,var)
else:
if var == 1 or var == 3:
return binMinMax(last,last,var)
else:
return binMinMax(first,first,var)
else:
mid = (first + last)//2
firstScore = getScore(fixed_y_test, train_classify(self.sensitive_attr, self.name, x_train_with_sensitive_feature, self.y_train, x_test_with_sensitive_feature, self.y_test, 1, first, self.x_control_test),var)
lastScore = getScore(fixed_y_test, train_classify(self.sensitive_attr, self.name, x_train_with_sensitive_feature, self.y_train, x_test_with_sensitive_feature, self.y_test, 1, last, self.x_control_test),var)
midY = y_classified_results = train_classify(self.sensitive_attr, self.name, x_train_with_sensitive_feature, self.y_train, x_test_with_sensitive_feature, self.y_test, 1, mid, self.x_control_test)
midScore = getScore(fixed_y_test, midY, var)

if midScore <= lastScore:
if var == 1 or var == 3:
return binMinMax(first,mid,var)
else:
return binMinMax(mid,last,var)
else:
if var == 1 or var == 3:
return binMinMax(mid,last,var)
else:
return binMinMax(first,mid,var)

'''
minDI = (1000,[])
maxacc = (1000,[])
maxDI = (1000,[])
if self.data=="ricci":
minDI = (50,[])
maxacc = (50,[])
maxDI = (50,[])
'''

# PRINT ETAS TO SHOW PLATEAU
if self.data == 'ricci':
Expand All @@ -169,16 +207,18 @@ def printScores():
scoresBoth.append(getScore(fixed_y_test,y_classified_results))
scoresAcc.append(getScore(fixed_y_test,y_classified_results, 2))
scoresDI.append(getScore(fixed_y_test,y_classified_results,3))
with open("etas/" + self.data + "-etas.csv",'w') as f:
f.write('Acc/DI, Acc, DI' + '\n')
with open("etas/" + self.data + "-etas-" + self.params['num'] + ".csv",'w') as f:
f.write('|1-DI|/Acc, Acc, |1-DI|' + '\n')
for i in range(len(scoresBoth)):
f.write(str(scoresBoth[i]) + ',' + str(scoresAcc[i]) + ',' + str(scoresDI[i]) +'\n')
f.close()
print("SCORES WRITTEN.")
#############################

'''
def binMaxVar(first, last, minDI, maxacc, maxDI, var=1): #var = 1-acc/DI, 2-acc, 3-DI
if first == last:
print(first)
if var == 1:
return minDI
elif var == 2:
Expand Down Expand Up @@ -232,16 +272,26 @@ def binMaxVar(first, last, minDI, maxacc, maxDI, var=1): #var = 1-acc/DI, 2-acc,
return binMaxVar(midpoint, last, minDI, maxacc, (secondMidScore, second_y_classified_results))
else:
return binMaxVar(first, midpoint, minDI, maxacc,maxDI)

printScores()
if self.data == "ricci":
final = binMaxVar(1,50, minDI, maxacc, maxDI, self.params['var'])
#final = (0, train_classify(self.sensitive_attr, self.name, x_train_with_sensitive_feature, self.y_train, x_test_with_sensitive_feature, self.y_test, 1, 1, self.x_control_test))
else:
final = binMaxVar(1,1000, minDI, maxacc, maxDI, self.params['var'])
predicted = final[1]
'''

if self.params['print'] == 1:
printScores()

if self.data == "ricci":
final = binMinMax(1,50,self.params['var'])
else:
final = binMinMax(1,1000,self.params['var'])

# predicted = train_classify(self.sensitive_attr, self.name, x_train_with_sensitive_feature, self.y_train, x_test_with_sensitive_feature, self.y_test, 1, 100, self.x_control_test)

kam_actual, kam_predicted, kam_protected = fixed_y_test, predicted, self.x_control_test[self.sensitive_attr]
kam_actual, kam_predicted, kam_protected = fixed_y_test, final, self.x_control_test[self.sensitive_attr]
kam_time = datetime.now() - startTime

return kam_actual, kam_predicted, kam_protected, kam_time
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2 changes: 2 additions & 0 deletions compare.txt
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Original file line number Diff line number Diff line change
@@ -0,0 +1,2 @@
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