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| import numpy as np | |
| from aif360.algorithms.inprocessing.celisMeta.General import General | |
| class FalseDiscovery(General): | |
| 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))) | |
| 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) | |