Request for Release of Bias Calculation Code

[Kqp1227]

Hi! I wanted to express my appreciation for your team's excellent work. Recently, while conducting experiments on "exp1 debiasing gender", I calculated a gender bias of "0.0546" using my own bias calculation code, which notably outperformed the "0.23±0.16" reported in your paper. This discrepancy has prompted concerns about the correctness of my implementation. Could you please consider releasing your bias calculation code for comparison and validation? It'd help me compare and verify my results. Looking forward to hearing from you. Thanks!!😊

[Kqp1227]

Already solved, thx!

[pkulium]

can I get the code?

[pkulium]

Already solved, thx!

can I get the code?

[Kqp1227]

Already solved, thx!

can I get the code?

sure, i think is this one, maybe u can have a try.

import pickle as pkl
import torch
import numpy as np
import csv


def load_results(test_results_path):
    with open(test_results_path, "rb") as f:
        results = pkl.load(f)
    return results

def process_results(face_indicators_all, gender_logits_all, race_logits_all, num_groups):
    
    bias = 0
    csv_file_path = 'male_bias_results.csv'
    with open(csv_file_path, 'w', newline='') as csvfile:
        csvwriter = csv.writer(csvfile)
        csvwriter.writerow(['Bias'])
        for i in face_indicators_all.keys():
            bias_single = 0
            freqs = np.zeros(num_groups)
            count = 0
            for j in range(len(face_indicators_all[i])):
                if face_indicators_all[i][j]:
                    count += 1
                    if num_groups == 2:  # Gender
                        # print(gender_logits_all[i][j])
                        gender_pred = torch.argmax(gender_logits_all[i][j].clone().detach()).item()
                        # print(gender_pred)
                        freqs[gender_pred] += 1
                        # print(gender_pred, freqs[gender_pred])
                    elif num_groups == 4:  # Race
                        race_pred = torch.argmax(race_logits_all[i][j].clone().detach()).item()
                        freqs[race_pred] += 1
                    elif num_groups == 8:  # Intersection of gender and race
                        gender_pred = torch.argmax(gender_logits_all[i][j].clone().detach()).item()
                        race_pred = torch.argmax(race_logits_all[i][j].clone().detach()).item()
                        intersection_group = gender_pred * 4 + race_pred
                        freqs[intersection_group] += 1
        
            if count > 0:
                freqs /= count  # Normalize frequencies

            id = 0
            for m in range(len(freqs)):
                for n in range(m):
                    # print("m:", m, "n:", n)
                    
                    bias += abs(freqs[n] - freqs[m])
                    bias_single += abs(freqs[n] - freqs[m])
                    # print(freqs[n], freqs[m])
            print("bias:", bias_single)
            csvwriter.writerow([bias_single])
            
    
    bias /= (0.5 * num_groups * (num_groups - 1))
    bias /= i

    return bias

def main(test_results_path):
    # Load the results
    face_indicators_all, face_bboxs_all, gender_logits_all, race_logits_all, age_logits_all = load_results(test_results_path)
    # Calculate biases
    gender_bias = process_results(face_indicators_all, gender_logits_all, race_logits_all, 2)
    race_bias = process_results(face_indicators_all, gender_logits_all, race_logits_all, 4)
    intersection_bias = process_results(face_indicators_all, gender_logits_all, race_logits_all, 8)
    
    print(gender_bias)
    print(race_bias)
    print(intersection_bias)
    
if __name__ == "__main__":
    test_results_path = "./outputs/base/test_results.pkl" #base
    test_results_path = "./exp_gen/test_results.pkl"
    main(test_results_path)