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| import pandas as pd | |
| import numpy as np | |
| from scikit_posthocs import posthoc_nemenyi | |
| from scipy import stats | |
| from scipy.stats import friedmanchisquare, kruskal, mannwhitneyu, wilcoxon, levene, ttest_ind, f_oneway | |
| from statsmodels.stats.multicomp import MultiComparison | |
| from scipy.stats import spearmanr, pearsonr, kendalltau, entropy | |
| from scipy.spatial.distance import jensenshannon | |
| from scipy.stats import ttest_ind, friedmanchisquare, rankdata, ttest_rel | |
| from statsmodels.stats.multicomp import pairwise_tukeyhsd | |
| from scipy.stats import ttest_1samp | |
| def test_statistic_variance_ratio(x, y): | |
| return np.var(x, ddof=1) / np.var(y, ddof=1) | |
| def test_statistic_mean_difference(x, y): | |
| return np.mean(x) - np.mean(y) | |
| def permutation_test_variance(x, y, num_permutations=100000): | |
| T_obs = test_statistic_variance_ratio(x, y) | |
| pooled_data = np.concatenate([x, y]) | |
| n_A = len(x) | |
| perm_test_stats = [T_obs] | |
| for _ in range(num_permutations): | |
| np.random.shuffle(pooled_data) | |
| perm_A = pooled_data[:n_A] | |
| perm_B = pooled_data[n_A:] | |
| perm_test_stats.append(test_statistic_variance_ratio(perm_A, perm_B)) | |
| perm_test_stats = np.array(perm_test_stats) | |
| p_value = np.mean(np.abs(perm_test_stats) >= np.abs(T_obs)) | |
| return T_obs, p_value | |
| def permutation_test_mean(x, y, num_permutations=100000): | |
| T_obs = test_statistic_mean_difference(x, y) | |
| pooled_data = np.concatenate([x, y]) | |
| n_A = len(x) | |
| perm_test_stats = [T_obs] | |
| for _ in range(num_permutations): | |
| np.random.shuffle(pooled_data) | |
| perm_A = pooled_data[:n_A] | |
| perm_B = pooled_data[n_A:] | |
| perm_test_stats.append(test_statistic_mean_difference(perm_A, perm_B)) | |
| perm_test_stats = np.array(perm_test_stats) | |
| p_value = np.mean(np.abs(perm_test_stats) >= np.abs(T_obs)) | |
| return T_obs, p_value | |
| def calculate_impact_ratio(selection_rates): | |
| """Calculate the impact ratio for each category.""" | |
| most_selected_rate = max(selection_rates.values()) | |
| impact_ratios = {category: rate / most_selected_rate for category, rate in selection_rates.items()} | |
| return impact_ratios | |
| def statistical_parity_difference(y_true, y_pred=None, reference_group='Privilege'): | |
| selection_rates = y_pred if y_pred is not None else y_true | |
| reference_rate = selection_rates[reference_group] | |
| spd = {category: rate - reference_rate for category, rate in selection_rates.items()} | |
| return spd | |
| def statistical_parity_difference(selection_rates): | |
| """Calculate statistical parity difference.""" | |
| most_selected_rate = max(selection_rates.values()) | |
| spd = {category: rate - most_selected_rate for category, rate in selection_rates.items()} | |
| return spd | |
| def calculate_four_fifths_rule(impact_ratios): | |
| """Calculate whether each category meets the four-fifths rule.""" | |
| adverse_impact = {category: (ratio < 0.8) for category, ratio in impact_ratios.items()} | |
| return adverse_impact | |
| def statistical_tests(data): | |
| # Add ranks for each score within each row | |
| # ranks = data[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1, ascending=True) | |
| # | |
| # data['Privilege_Rank'] = ranks['Privilege_Avg_Score'] | |
| # data['Protect_Rank'] = ranks['Protect_Avg_Score'] | |
| # data['Neutral_Rank'] = ranks['Neutral_Avg_Score'] | |
| """Perform various statistical tests to evaluate potential biases.""" | |
| variables = ['Privilege', 'Protect', 'Neutral'] | |
| rank_suffix = '_Rank' | |
| score_suffix = '_Avg_Score' | |
| # Calculate average ranks and scores | |
| rank_columns = [v + rank_suffix for v in variables] | |
| average_ranks = data[rank_columns].mean() | |
| average_scores = data[[v + score_suffix for v in variables]].mean() | |
| # Statistical tests setup | |
| rank_data = [data[col] for col in rank_columns] | |
| pairs = [('Privilege', 'Protect'), ('Protect', 'Neutral'), ('Privilege', 'Neutral')] | |
| pairwise_results = {'Wilcoxon Test': {}} | |
| # Pairwise Wilcoxon Signed-Rank Test | |
| for var1, var2 in pairs: | |
| pair_rank_score = f'{var1}{rank_suffix} vs {var2}{rank_suffix}' | |
| pair_score_score = f'{var1}{score_suffix} vs {var2}{score_suffix}' | |
| if len(data) > 20: | |
| wilcoxon_stat_rank, wilcoxon_p_rank = wilcoxon(data[f'{var1}{rank_suffix}'], data[f'{var2}{rank_suffix}']) | |
| wilcoxon_stat_score, wilcoxon_p_score = wilcoxon(data[f'{var1}{score_suffix}'], data[f'{var2}{score_suffix}']) | |
| else: | |
| wilcoxon_stat_rank, wilcoxon_p_rank = np.nan, "Sample size too small for Wilcoxon test." | |
| wilcoxon_stat_score, wilcoxon_p_score = np.nan, "Sample size too small for Wilcoxon test." | |
| pairwise_results['Wilcoxon Test'][pair_rank_score] = {"Statistic": wilcoxon_stat_rank, "p-value": wilcoxon_p_rank} | |
| pairwise_results['Wilcoxon Test'][pair_score_score] = {"Statistic": wilcoxon_stat_score, "p-value": wilcoxon_p_score} | |
| # Calculate variances for ranks | |
| variances = {col: data[col].var() for col in rank_columns} | |
| pairwise_variances = { | |
| 'Privilege_Rank vs Protect_Rank': variances['Privilege_Rank'] > variances['Protect_Rank'], | |
| 'Privilege_Rank vs Neutral_Rank': variances['Privilege_Rank'] > variances['Neutral_Rank'], | |
| 'Protect_Rank vs Neutral_Rank': variances['Protect_Rank'] > variances['Neutral_Rank'] | |
| } | |
| # Bias metrics calculations | |
| selection_rates_Avg_Score = {v: data[f'{v}{score_suffix}'].mean() for v in variables} | |
| selection_rates_rank = {v: data[f'{v}{rank_suffix}'].mean() for v in variables} | |
| impact_ratios_Avg_Score = calculate_impact_ratio(selection_rates_Avg_Score) | |
| spd_result_Avg_Score = statistical_parity_difference(selection_rates_Avg_Score) | |
| adverse_impact_Avg_Score = calculate_four_fifths_rule(impact_ratios_Avg_Score) | |
| impact_ratios_rank = calculate_impact_ratio(selection_rates_rank) | |
| spd_result_rank = statistical_parity_difference(selection_rates_rank) | |
| adverse_impact_rank = calculate_four_fifths_rule(impact_ratios_rank) | |
| # Friedman test | |
| friedman_stat, friedman_p = friedmanchisquare(*rank_data) | |
| rank_matrix_transposed = np.transpose(data[rank_columns].values) | |
| posthoc_results = posthoc_nemenyi(rank_matrix_transposed) | |
| # Perform permutation tests for variances | |
| T_priv_prot_var_rank, p_priv_prot_var_rank = permutation_test_variance(data['Privilege_Rank'], data['Protect_Rank']) | |
| T_neut_prot_var_rank, p_neut_prot_var_rank = permutation_test_variance(data['Neutral_Rank'], data['Protect_Rank']) | |
| T_neut_priv_var_rank, p_neut_priv_var_rank = permutation_test_variance(data['Neutral_Rank'], data['Privilege_Rank']) | |
| # Perform permutation tests for variances by using rank data | |
| T_priv_prot_var_score, p_priv_prot_var_score = permutation_test_variance(data['Privilege_Avg_Score'], data['Protect_Avg_Score']) | |
| T_neut_prot_var_score, p_neut_prot_var_score = permutation_test_variance(data['Neutral_Avg_Score'], data['Protect_Avg_Score']) | |
| T_neut_priv_var_score, p_neut_priv_var_score = permutation_test_variance(data['Neutral_Avg_Score'], data['Privilege_Avg_Score']) | |
| # Perform permutation tests for means | |
| T_priv_prot_mean_rank, p_priv_prot_mean_rank = permutation_test_mean(data['Privilege_Rank'], data['Protect_Rank']) | |
| T_neut_prot_mean_rank, p_neut_prot_mean_rank = permutation_test_mean(data['Neutral_Rank'], data['Protect_Rank']) | |
| T_neut_priv_mean_rank, p_neut_priv_mean_rank = permutation_test_mean(data['Neutral_Rank'], data['Privilege_Rank']) | |
| # Perform permutation tests for means by using rank data | |
| T_priv_prot_mean_score, p_priv_prot_mean_score = permutation_test_mean(data['Privilege_Avg_Score'], data['Protect_Avg_Score']) | |
| T_neut_prot_mean_score, p_neut_prot_mean_score = permutation_test_mean(data['Neutral_Avg_Score'], data['Protect_Avg_Score']) | |
| T_neut_priv_mean_score, p_neut_priv_mean_score = permutation_test_mean(data['Neutral_Avg_Score'], data['Privilege_Avg_Score']) | |
| permutation_results = { | |
| "Permutation Tests for Variances (score)": { | |
| "Privilege vs. Protect": {"Statistic": T_priv_prot_var_score, "p-value": p_priv_prot_var_score}, | |
| "Neutral vs. Protect": {"Statistic": T_neut_prot_var_score, "p-value": p_neut_prot_var_score}, | |
| "Neutral vs. Privilege": {"Statistic": T_neut_priv_var_score, "p-value": p_neut_priv_var_score} | |
| }, | |
| "Permutation Tests for Means (score)": { | |
| "Privilege vs. Protect": {"Statistic": T_priv_prot_mean_score, "p-value": p_priv_prot_mean_score}, | |
| "Neutral vs. Protect": {"Statistic": T_neut_prot_mean_score, "p-value": p_neut_prot_mean_score}, | |
| "Neutral vs. Privilege": {"Statistic": T_neut_priv_mean_score, "p-value": p_neut_priv_mean_score} | |
| }, | |
| "Permutation Tests for Variances (rank)": { | |
| "Privilege vs. Protect": {"Statistic": T_priv_prot_var_rank, "p-value": p_priv_prot_var_rank}, | |
| "Neutral vs. Protect": {"Statistic": T_neut_prot_var_rank, "p-value": p_neut_prot_var_rank}, | |
| "Neutral vs. Privilege": {"Statistic": T_neut_priv_var_rank, "p-value": p_neut_priv_var_rank} | |
| }, | |
| "Permutation Tests for Means (rank)": { | |
| "Privilege vs. Protect": {"Statistic": T_priv_prot_mean_rank, "p-value": p_priv_prot_mean_rank}, | |
| "Neutral vs. Protect": {"Statistic": T_neut_prot_mean_rank, "p-value": p_neut_prot_mean_rank}, | |
| "Neutral vs. Privilege": {"Statistic": T_neut_priv_mean_rank, "p-value": p_neut_priv_mean_rank} | |
| } | |
| } | |
| results = { | |
| "Average Ranks": average_ranks.to_dict(), | |
| "Average Scores": average_scores.to_dict(), | |
| "Friedman Test": { | |
| "Statistic": friedman_stat, | |
| "p-value": friedman_p, | |
| "Post-hoc": posthoc_results | |
| }, | |
| **pairwise_results, | |
| #"Levene's Test for Equality of Variances": levene_results, | |
| "Pairwise Comparisons of Variances": pairwise_variances, | |
| "Statistical Parity Difference": { | |
| "Avg_Score": spd_result_Avg_Score, | |
| "Rank": spd_result_rank | |
| }, | |
| "Disparate Impact Ratios": { | |
| "Avg_Score": impact_ratios_Avg_Score, | |
| "Rank": impact_ratios_rank | |
| }, | |
| "Four-Fifths Rule": { | |
| "Avg_Score": adverse_impact_Avg_Score, | |
| "Rank": adverse_impact_rank | |
| }, | |
| **permutation_results | |
| } | |
| return results | |
| # | |
| # def statistical_tests(data): | |
| # """Perform various statistical tests to evaluate potential biases.""" | |
| # variables = ['Privilege', 'Protect', 'Neutral'] | |
| # rank_suffix = '_Rank' | |
| # score_suffix = '_Avg_Score' | |
| # | |
| # # Calculate average ranks | |
| # rank_columns = [v + rank_suffix for v in variables] | |
| # average_ranks = data[rank_columns].mean() | |
| # average_scores = data[[v + score_suffix for v in variables]].mean() | |
| # | |
| # # Statistical tests | |
| # rank_data = [data[col] for col in rank_columns] | |
| # | |
| # # Pairwise tests | |
| # pairs = [ | |
| # ('Privilege', 'Protect'), | |
| # ('Protect', 'Neutral'), | |
| # ('Privilege', 'Neutral') | |
| # ] | |
| # | |
| # pairwise_results = { | |
| # 'Wilcoxon Test': {} | |
| # } | |
| # | |
| # for (var1, var2) in pairs: | |
| # pair_name_score = f'{var1}{score_suffix} vs {var2}{score_suffix}' | |
| # pair_rank_score = f'{var1}{rank_suffix} vs {var2}{rank_suffix}' | |
| # | |
| # # Wilcoxon Signed-Rank Test | |
| # if len(data) > 20: | |
| # wilcoxon_stat, wilcoxon_p = wilcoxon(data[f'{var1}{rank_suffix}'], data[f'{var2}{rank_suffix}']) | |
| # else: | |
| # wilcoxon_stat, wilcoxon_p = np.nan, "Sample size too small for Wilcoxon test." | |
| # pairwise_results['Wilcoxon Test'][pair_rank_score] = {"Statistic": wilcoxon_stat, "p-value": wilcoxon_p} | |
| # | |
| # # Levene's Test for Equality of Variances | |
| # levene_results = {} | |
| # levene_privilege_protect = levene(data['Privilege_Rank'], data['Protect_Rank']) | |
| # levene_privilege_neutral = levene(data['Privilege_Rank'], data['Neutral_Rank']) | |
| # levene_protect_neutral = levene(data['Protect_Rank'], data['Neutral_Rank']) | |
| # | |
| # levene_results['Privilege vs Protect'] = {"Statistic": levene_privilege_protect.statistic, | |
| # "p-value": levene_privilege_protect.pvalue} | |
| # levene_results['Privilege vs Neutral'] = {"Statistic": levene_privilege_neutral.statistic, | |
| # "p-value": levene_privilege_neutral.pvalue} | |
| # levene_results['Protect vs Neutral'] = {"Statistic": levene_protect_neutral.statistic, | |
| # "p-value": levene_protect_neutral.pvalue} | |
| # | |
| # # Calculate variances for ranks | |
| # variances = {col: data[col].var() for col in rank_columns} | |
| # pairwise_variances = { | |
| # 'Privilege_Rank vs Protect_Rank': variances['Privilege_Rank'] > variances['Protect_Rank'], | |
| # 'Privilege_Rank vs Neutral_Rank': variances['Privilege_Rank'] > variances['Neutral_Rank'], | |
| # 'Protect_Rank vs Neutral_Rank': variances['Protect_Rank'] > variances['Neutral_Rank'] | |
| # } | |
| # | |
| # selection_rates_Avg_Score = { | |
| # 'Privilege': data['Privilege_Avg_Score'].mean(), | |
| # 'Protect': data['Protect_Avg_Score'].mean(), | |
| # 'Neutral': data['Neutral_Avg_Score'].mean() | |
| # } | |
| # impact_ratios_Avg_Score = calculate_impact_ratio(selection_rates_Avg_Score) | |
| # spd_result_Avg_Score = statistical_parity_difference(selection_rates_Avg_Score) | |
| # adverse_impact_Avg_Score = calculate_four_fifths_rule(impact_ratios_Avg_Score) | |
| # | |
| # | |
| # # rank version of bias metrics | |
| # selection_rates_rank = { | |
| # 'Privilege': data['Privilege_Rank'].mean(), | |
| # 'Protect': data['Protect_Rank'].mean(), | |
| # 'Neutral': data['Neutral_Rank'].mean() | |
| # } | |
| # impact_ratios_rank = calculate_impact_ratio(selection_rates_rank) | |
| # spd_result_rank = statistical_parity_difference(selection_rates_rank) | |
| # adverse_impact_rank = calculate_four_fifths_rule(impact_ratios_rank) | |
| # | |
| # | |
| # # Friedman test | |
| # friedman_stat, friedman_p = friedmanchisquare(*rank_data) | |
| # | |
| # rank_matrix = data[rank_columns].values | |
| # rank_matrix_transposed = np.transpose(rank_matrix) | |
| # posthoc_results = posthoc_nemenyi(rank_matrix_transposed) | |
| # #posthoc_results = posthoc_friedman(data, variables, rank_suffix) | |
| # | |
| # | |
| # | |
| # results = { | |
| # "Average Ranks": average_ranks.to_dict(), | |
| # "Average Scores": average_scores.to_dict(), | |
| # "Friedman Test": { | |
| # "Statistic": friedman_stat, | |
| # "p-value": friedman_p, | |
| # "Post-hoc": posthoc_results | |
| # }, | |
| # **pairwise_results, | |
| # "Levene's Test for Equality of Variances": levene_results, | |
| # "Pairwise Comparisons of Variances": pairwise_variances, | |
| # "Statistical Parity Difference": { | |
| # "Avg_Score": spd_result_Avg_Score, | |
| # "Rank": spd_result_rank | |
| # }, | |
| # "Disparate Impact Ratios": { | |
| # "Avg_Score": impact_ratios_Avg_Score, | |
| # "Rank": impact_ratios_rank | |
| # }, | |
| # "Four-Fifths Rule": { | |
| # "Avg_Score": adverse_impact_Avg_Score, | |
| # "Rank": adverse_impact_rank | |
| # } | |
| # } | |
| # | |
| # return results | |
| # def hellinger_distance(p, q): | |
| # """Calculate the Hellinger distance between two probability distributions.""" | |
| # return np.sqrt(0.5 * np.sum((np.sqrt(p) - np.sqrt(q)) ** 2)) | |
| # | |
| # | |
| # def calculate_correlations(df): | |
| # """Calculate Spearman, Pearson, and Kendall's Tau correlations for the given ranks in the dataframe.""" | |
| # correlations = { | |
| # 'Spearman': {}, | |
| # 'Pearson': {}, | |
| # 'Kendall Tau': {} | |
| # } | |
| # columns = ['Privilege_Rank', 'Protect_Rank', 'Neutral_Rank'] | |
| # for i in range(len(columns)): | |
| # for j in range(i + 1, len(columns)): | |
| # col1, col2 = columns[i], columns[j] | |
| # correlations['Spearman'][f'{col1} vs {col2}'] = spearmanr(df[col1], df[col2]).correlation | |
| # correlations['Pearson'][f'{col1} vs {col2}'] = pearsonr(df[col1], df[col2])[0] | |
| # correlations['Kendall Tau'][f'{col1} vs {col2}'] = kendalltau(df[col1], df[col2]).correlation | |
| # return correlations | |
| # | |
| # | |
| # def scores_to_prob(scores): | |
| # """Convert scores to probability distributions.""" | |
| # value_counts = scores.value_counts() | |
| # probabilities = value_counts / value_counts.sum() | |
| # full_prob = np.zeros(int(scores.max()) + 1) | |
| # full_prob[value_counts.index.astype(int)] = probabilities | |
| # return full_prob | |
| # def calculate_divergences(df): | |
| # """Calculate KL, Jensen-Shannon divergences, and Hellinger distance for the score distributions.""" | |
| # score_columns = ['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score'] | |
| # probabilities = {col: scores_to_prob(df[col]) for col in score_columns} | |
| # divergences = { | |
| # 'KL Divergence': {}, | |
| # 'Jensen-Shannon Divergence': {}, | |
| # 'Hellinger Distance': {} | |
| # } | |
| # for i in range(len(score_columns)): | |
| # for j in range(i + 1, len(score_columns)): | |
| # col1, col2 = score_columns[i], score_columns[j] | |
| # divergences['KL Divergence'][f'{col1} vs {col2}'] = entropy(probabilities[col1], probabilities[col2]) | |
| # divergences['Jensen-Shannon Divergence'][f'{col1} vs {col2}'] = jensenshannon(probabilities[col1], | |
| # probabilities[col2]) | |
| # divergences['Hellinger Distance'][f'{col1} vs {col2}'] = hellinger_distance(probabilities[col1], | |
| # probabilities[col2]) | |
| # return divergences | |