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Vectorized MetaFairClassifier and removed unused code #196

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Feb 12, 2021
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Vectorized MetaFairClassifier and removed unused code #196

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240d23e
vectorized meta fair and removed unused code
hoffmansc Aug 6, 2020
5abde89
Merge branch 'master' into meta_fair_cleanup
hoffmansc Nov 19, 2020
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205 changes: 59 additions & 146 deletions aif360/algorithms/inprocessing/celisMeta/FalseDiscovery.py
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Original file line number Diff line number Diff line change
@@ -1,151 +1,64 @@
from __future__ import division

import os,sys
from scipy.stats import multivariate_normal
import scipy.stats as st
import numpy as np
import math

import site
site.addsitedir('.')

from .General import *
from . import utils as ut
from aif360.algorithms.inprocessing.celisMeta.General import General


class FalseDiscovery(General):

def getExpectedGrad(self, dist_params, params, samples, mu, z_0, z_1, a, b):
u_1, u_2, l_1, l_2 = params[0], params[1], params[2], params[3]
a, b = a[0], b[0]
res1 = []
res2 = []
res3 = []
res4 = []
for x in samples:
temp = np.append(np.append(x, 1), 1)
prob_1_1 = ut.getProbability(dist_params, temp)

temp = np.append(np.append(x, -1), 1)
prob_m1_1 = ut.getProbability(dist_params, temp)

temp = np.append(np.append(x, 1), 0)
prob_1_0 = ut.getProbability(dist_params, temp)

temp = np.append(np.append(x, -1), 0)
prob_m1_0 = ut.getProbability(dist_params, temp)


prob_y_1 = (prob_1_1 + prob_1_0) / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)
#print(prob_y_1)

prob_z_0 = (prob_m1_0 + prob_1_0) / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)
prob_z_1 = (prob_m1_1 + prob_1_1) / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)

probc_m1_0 = prob_m1_0 / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)
probc_m1_1 = prob_m1_1 / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)

c_0 = prob_y_1 - 0.5
c_1 = u_1 * (probc_m1_0 - a*prob_z_0) + u_2 * (probc_m1_1 - a*prob_z_1)
c_2 = l_1 * (- probc_m1_0 + b*prob_z_0) + l_2 * (- probc_m1_1 + b*prob_z_1)

t = math.sqrt((c_0 + c_1 + c_2)*(c_0 + c_1 + c_2) + mu*mu)
t1 = (c_0 + c_1 + c_2) * (probc_m1_0 - a*prob_z_0)/t
t2 = (c_0 + c_1 + c_2) * (probc_m1_1 - a*prob_z_1)/t
t3 = (c_0 + c_1 + c_2) * (- probc_m1_0 + b*prob_z_0)/t
t4 = (c_0 + c_1 + c_2) * (- probc_m1_1 + b*prob_z_1)/t
#print(t1,t2)
res1.append(t1)
res2.append(t2)
res3.append(t3)
res4.append(t4)

return [np.mean(res1), np.mean(res2), np.mean(res3), np.mean(res4)]

def getValueForX(self, dist_params, a,b, params, samples, z_0, z_1, x, flag):
u_1, u_2, l_1, l_2 = params[0], params[1], params[2], params[3]
#print (params)
a, b = a[0], b[0]

temp = np.append(np.append(x, 1), 1)
prob_1_1 = ut.getProbability(dist_params, temp)

temp = np.append(np.append(x, -1), 1)
prob_m1_1 = ut.getProbability(dist_params, temp)

temp = np.append(np.append(x, 1), 0)
prob_1_0 = ut.getProbability(dist_params, temp)

temp = np.append(np.append(x, -1), 0)
prob_m1_0 = ut.getProbability(dist_params, temp)

if (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1) == 0:
print("Probability is 0.\n")
return 0

prob_y_1 = (prob_1_1 + prob_1_0) / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)
#print(prob_y_1)

prob_z_0 = (prob_m1_0 + prob_1_0) / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)
prob_z_1 = (prob_m1_1 + prob_1_1) / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)


probc_m1_0 = prob_m1_0 / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)
probc_m1_1 = prob_m1_1 / (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)

c_0 = prob_y_1 - 0.5
c_1 = u_1 * (probc_m1_0 - a*prob_z_0) + u_2 * (probc_m1_1 - a*prob_z_1)
c_2 = l_1 * (- probc_m1_0 + b*prob_z_0) + l_2 * (- probc_m1_1 + b*prob_z_1)
if flag==1:
print(c_0, c_1, c_2, prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1)

# c_1 = prob_z_0/z_0
# c_2 = prob_z_1/z_1

t = c_0 + c_1 + c_2
return t

def getFuncValue(self, dist_params, a,b, params, samples, z_0, z_1):
res = []
for x in samples:
t = abs(self.getValueForX(dist_params, a,b, params, samples, z_0, z_1, x, 0))
res.append(t)

exp = np.mean(res)
return exp

def getNumOfParams(self):
return 4

def getGamma(self, y_test, y_res, x_control_test):
pos_0 = 0
pos_1 = 0

z1_0 = 0
z1_1 = 0
for j in range(0,len(y_test)):
result = y_res[j]

if result == 1 and x_control_test[j] == 0:
z1_0 += 1
if result == 1 and x_control_test[j] == 1:
z1_1 += 1

actual = y_test[j]
if result == 1 and actual == -1 and x_control_test[j] == 0:
pos_0 += 1
if result == 1 and actual == -1 and x_control_test[j] == 1:
pos_1 += 1

pos_0 = float(pos_0)/z1_0
pos_1 = float(pos_1)/z1_1
if pos_0 == 0 or pos_1 == 0:
return 0
else:
return min(pos_0/pos_1 , pos_1/pos_0)


if __name__ == '__main__':
obj = FalseDiscovery()
obj.testPreprocessedData()
#obj.testSyntheticData()
def getExpectedGrad(self, dist, a, b, params, samples, mu, z_prior):
t, probc_m1_0, probc_m1_1, prob_z_0, prob_z_1 = self.getValueForX(dist,
a, b, params, z_prior, samples, return_probs=True)
res = np.vstack([probc_m1_0 - a*prob_z_0,
probc_m1_1 - a*prob_z_1,
-probc_m1_0 + b*prob_z_0,
-probc_m1_1 + b*prob_z_1])
res *= t / np.sqrt(t**2 + mu**2)
return np.mean(res, axis=1)

def getValueForX(self, dist, a, b, params, z_prior, x, return_probs=False):
u_1, u_2, l_1, l_2 = params
z_0, z_1 = 1-z_prior, z_prior

pos = np.ones(len(x))
prob_1_1 = self.prob(dist, np.c_[x, pos, pos])
prob_m1_1 = self.prob(dist, np.c_[x, -pos, pos])
prob_1_0 = self.prob(dist, np.c_[x, pos, np.zeros(len(x))])
prob_m1_0 = self.prob(dist, np.c_[x, -pos, np.zeros(len(x))])

total = prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1
# if total == 0:
# return 0

prob_y_1 = (prob_1_1 + prob_1_0) / total
prob_z_0 = (prob_m1_0 + prob_1_0) / total
prob_z_1 = (prob_m1_1 + prob_1_1) / total

probc_m1_0 = prob_m1_0 / total
probc_m1_1 = prob_m1_1 / total

c_0 = prob_y_1 - 0.5
c_1 = u_1*(probc_m1_0 - a*prob_z_0) + u_2*(probc_m1_1 - a*prob_z_1)
c_2 = l_1*(-probc_m1_0 + b*prob_z_0) + l_2*(-probc_m1_1 + b*prob_z_1)

t = c_0 + c_1 + c_2
if return_probs:
return t, probc_m1_0, probc_m1_1, prob_z_0, prob_z_1
return t

def getFuncValue(self, dist, a, b, params, samples, z_prior):
return np.mean(np.abs(self.getValueForX(dist, a, b, params, z_prior,
samples)))

@property
def num_params(self):
return 4

def gamma(self, y_true, y_pred, sens):
pos_0 = y_pred[sens == 0] == 1
pos_1 = y_pred[sens == 1] == 1
if np.sum(pos_0) == 0 or np.sum(pos_1) == 0:
return 0
fdr_0 = np.sum(pos_0 & (y_true[sens == 0] == -1)) / np.sum(pos_0)
fdr_1 = np.sum(pos_1 & (y_true[sens == 1] == -1)) / np.sum(pos_1)
if fdr_0 == 0 or fdr_1 == 0:
return 0
return min(fdr_0/fdr_1, fdr_1/fdr_0)
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