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vectorized meta fair and removed unused code
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aif360/algorithms/inprocessing/celisMeta/FalseDiscovery.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,151 +1,64 @@ | ||
| from __future__ import division | ||
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| import os,sys | ||
| from scipy.stats import multivariate_normal | ||
| import scipy.stats as st | ||
| import numpy as np | ||
| import math | ||
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| import site | ||
| site.addsitedir('.') | ||
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| from .General import * | ||
| from . import utils as ut | ||
| from aif360.algorithms.inprocessing.celisMeta.General import General | ||
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| class FalseDiscovery(General): | ||
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| 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) | ||
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| temp = np.append(np.append(x, -1), 1) | ||
| prob_m1_1 = ut.getProbability(dist_params, temp) | ||
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| temp = np.append(np.append(x, 1), 0) | ||
| prob_1_0 = ut.getProbability(dist_params, temp) | ||
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| temp = np.append(np.append(x, -1), 0) | ||
| prob_m1_0 = ut.getProbability(dist_params, temp) | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| return [np.mean(res1), np.mean(res2), np.mean(res3), np.mean(res4)] | ||
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| 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] | ||
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| temp = np.append(np.append(x, 1), 1) | ||
| prob_1_1 = ut.getProbability(dist_params, temp) | ||
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| temp = np.append(np.append(x, -1), 1) | ||
| prob_m1_1 = ut.getProbability(dist_params, temp) | ||
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| temp = np.append(np.append(x, 1), 0) | ||
| prob_1_0 = ut.getProbability(dist_params, temp) | ||
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| temp = np.append(np.append(x, -1), 0) | ||
| prob_m1_0 = ut.getProbability(dist_params, temp) | ||
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| if (prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1) == 0: | ||
| print("Probability is 0.\n") | ||
| return 0 | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| 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) | ||
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| # c_1 = prob_z_0/z_0 | ||
| # c_2 = prob_z_1/z_1 | ||
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| t = c_0 + c_1 + c_2 | ||
| return t | ||
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| 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) | ||
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| exp = np.mean(res) | ||
| return exp | ||
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| def getNumOfParams(self): | ||
| return 4 | ||
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| def getGamma(self, y_test, y_res, x_control_test): | ||
| pos_0 = 0 | ||
| pos_1 = 0 | ||
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| z1_0 = 0 | ||
| z1_1 = 0 | ||
| for j in range(0,len(y_test)): | ||
| result = y_res[j] | ||
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| if result == 1 and x_control_test[j] == 0: | ||
| z1_0 += 1 | ||
| if result == 1 and x_control_test[j] == 1: | ||
| z1_1 += 1 | ||
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| 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 | ||
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| 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) | ||
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| 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) | ||
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| 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 | ||
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| 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))]) | ||
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| total = prob_1_1 + prob_1_0 + prob_m1_0 + prob_m1_1 | ||
| # if total == 0: | ||
| # return 0 | ||
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| 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 | ||
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| probc_m1_0 = prob_m1_0 / total | ||
| probc_m1_1 = prob_m1_1 / total | ||
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| 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) | ||
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| 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 | ||
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| def getFuncValue(self, dist, a, b, params, samples, z_prior): | ||
| return np.mean(np.abs(self.getValueForX(dist, a, b, params, z_prior, | ||
| samples))) | ||
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| @property | ||
| def num_params(self): | ||
| return 4 | ||
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| 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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