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Aequitas_Semi_Directed_Sklearn.py
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Aequitas_Semi_Directed_Sklearn.py
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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 numpy as np
import loss_funcs as lf # loss funcs that can be optimized subject to various constraints
import random
import time
from scipy.optimize import basinhopping
import config
from sklearn.externals import joblib
random.seed(time.time())
start_time = time.time()
init_prob = 0.5
params = config.params
direction_probability = [init_prob] * params
direction_probability_change_size = 0.001
sensitive_param = config.sensitive_param
name = 'sex'
cov = 0
perturbation_unit = config.perturbation_unit
threshold = config.threshold
global_disc_inputs = set()
global_disc_inputs_list = []
local_disc_inputs = set()
local_disc_inputs_list = []
tot_inputs = set()
global_iteration_limit = 1000
local_iteration_limit = 1000
input_bounds = config.input_bounds
classifier_name = config.classifier_name
model = joblib.load(classifier_name)
class Local_Perturbation(object):
def __init__(self, stepsize=1):
self.stepsize = stepsize
def __call__(self, x):
s = self.stepsize
param_choice = random.randint(0, 12)
act = [-1, 1]
direction_choice = np.random.choice(act, p=[direction_probability[param_choice], (1 - direction_probability[param_choice])])
if (x[param_choice] == input_bounds[param_choice][0]) or (x[param_choice] == input_bounds[param_choice][1]):
direction_choice = np.random.choice(act)
x[param_choice] = x[param_choice] + (direction_choice * perturbation_unit)
x[param_choice] = max(input_bounds[param_choice][0], x[param_choice])
x[param_choice] = min(input_bounds[param_choice][1], x[param_choice])
ei = evaluate_input(x)
if (ei and direction_choice == -1) or (not ei and direction_choice == 1):
direction_probability[param_choice] = min(
direction_probability[param_choice] + (direction_probability_change_size * perturbation_unit), 1)
elif (not ei and direction_choice == -1) or (ei and direction_choice == 1):
direction_probability[param_choice] = max(
direction_probability[param_choice] - (direction_probability_change_size * perturbation_unit), 0)
return x
class Global_Discovery(object):
def __init__(self, stepsize=1):
self.stepsize = stepsize
def __call__(self, x):
s = self.stepsize
for i in xrange(params):
random.seed(time.time())
x[i] = random.randint(input_bounds[i][0], input_bounds[i][1])
x[sensitive_param - 1] = 0
# print x
return x
def evaluate_input(inp):
inp0 = [int(i) for i in inp]
inp1 = [int(i) for i in inp]
inp0[sensitive_param - 1] = 0
inp1[sensitive_param - 1] = 1
inp0 = np.asarray(inp0)
inp0 = np.reshape(inp0, (1, -1))
inp1 = np.asarray(inp1)
inp1 = np.reshape(inp1, (1, -1))
out0 = model.predict(inp0)
out1 = model.predict(inp1)
return (abs(out0 - out1) > threshold)
# for binary classification, we have found that the
# following optimization function gives better results
# return abs(out1 + out0)
def evaluate_global(inp):
inp0 = [int(i) for i in inp]
inp1 = [int(i) for i in inp]
inp0[sensitive_param - 1] = 0
inp1[sensitive_param - 1] = 1
inp0 = np.asarray(inp0)
inp0 = np.reshape(inp0, (1, -1))
inp1 = np.asarray(inp1)
inp1 = np.reshape(inp1, (1, -1))
out0 = model.predict(inp0)
out1 = model.predict(inp1)
tot_inputs.add(tuple(map(tuple, inp0)))
if (abs(out0 - out1) > threshold and tuple(map(tuple, inp0)) not in global_disc_inputs):
global_disc_inputs.add(tuple(map(tuple, inp0)))
global_disc_inputs_list.append(inp0.tolist()[0])
return not abs(out0 - out1) > threshold
# for binary classification, we have found that the
# following optimization function gives better results
# return abs(out1 + out0)
def evaluate_local(inp):
inp0 = [int(i) for i in inp]
inp1 = [int(i) for i in inp]
inp0[sensitive_param - 1] = 0
inp1[sensitive_param - 1] = 1
inp0 = np.asarray(inp0)
inp0 = np.reshape(inp0, (1, -1))
inp1 = np.asarray(inp1)
inp1 = np.reshape(inp1, (1, -1))
out0 = model.predict(inp0)
out1 = model.predict(inp1)
tot_inputs.add(tuple(map(tuple, inp0)))
if (abs(out0 - out1) > threshold and (tuple(map(tuple, inp0)) not in global_disc_inputs)
and (tuple(map(tuple, inp0)) not in local_disc_inputs)):
local_disc_inputs.add(tuple(map(tuple, inp0)))
local_disc_inputs_list.append(inp0.tolist()[0])
return not abs(out0 - out1) > threshold
# for binary classification, we have found that the
# following optimization function gives better results
# return abs(out1 + out0)
initial_input = [7, 4, 26, 1, 4, 4, 0, 0, 0, 1, 5, 73, 1]
minimizer = {"method": "L-BFGS-B"}
global_discovery = Global_Discovery()
local_perturbation = Local_Perturbation()
basinhopping(evaluate_global, initial_input, stepsize=1.0, take_step=global_discovery, minimizer_kwargs=minimizer,
niter=global_iteration_limit)
print "Finished Global Search"
print "Percentage discriminatory inputs - " + str(float(len(global_disc_inputs_list)
+ len(local_disc_inputs_list)) / float(len(tot_inputs))*100)
print ""
print "Starting Local Search"
for inp in global_disc_inputs_list:
basinhopping(evaluate_local, inp, stepsize=1.0, take_step=local_perturbation, minimizer_kwargs=minimizer,
niter=local_iteration_limit)
print "Percentage discriminatory inputs - " + str(float(len(global_disc_inputs_list) + len(local_disc_inputs_list))
/ float(len(tot_inputs))*100)
print ""
print "Local Search Finished"
print "Percentage discriminatory inputs - " + str(float(len(global_disc_inputs_list) + len(local_disc_inputs_list))
/ float(len(tot_inputs))*100)
print ""
print "Total Inputs are " + str(len(tot_inputs))
print "Number of discriminatory inputs are " + str(len(global_disc_inputs_list)+len(local_disc_inputs_list))