Spaces:
Sleeping
Sleeping
File size: 5,936 Bytes
5defafa 40d7b09 5defafa 0765d8d e770ab5 fcfc515 e770ab5 5fd4442 40d7b09 5fd4442 40d7b09 168431b 40d7b09 180622c ba8c4f4 180622c 168431b 5ca3be7 168431b 16d8bbb 168431b 16d8bbb 40d7b09 168431b 16d8bbb 5ca3be7 40d7b09 ba8c4f4 40d7b09 5fd4442 fcfc515 ba8c4f4 40d7b09 5fd4442 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
import pandas as pd
import numpy as np
from scipy.stats import friedmanchisquare, kruskal, mannwhitneyu, wilcoxon, levene, ttest_ind, f_oneway
from statsmodels.stats.multicomp import MultiComparison
import pandas as pd
import numpy as np
from scipy.stats import spearmanr, pearsonr, kendalltau, entropy
from scipy.spatial.distance import jensenshannon
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
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()
# Statistical tests
rank_data = [data[col] for col in rank_columns]
kw_stat, kw_p = kruskal(*rank_data)
# Pairwise tests
pairwise_results = {}
pairs = [
('Privilege', 'Protect'),
('Protect', 'Neutral'),
('Privilege', 'Neutral')
]
pairwise_results = {
# 'Mann-Whitney U Test': {},
# 'Wilcoxon Test': {},
# 'Levene\'s Test': {},
'T-Test': {}
}
for (var1, var2) in pairs:
pair_name_rank = f'{var1}{rank_suffix} vs {var2}{rank_suffix}'
pair_name_score = f'{var1}{score_suffix} vs {var2}{score_suffix}'
# # Mann-Whitney U Test
# mw_stat, mw_p = mannwhitneyu(data[f'{var1}{rank_suffix}'], data[f'{var2}{rank_suffix}'])
# pairwise_results['Mann-Whitney U Test'][pair_name_rank] = {"Statistic": mw_stat, "p-value": mw_p}
#
# # 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_name_rank] = {"Statistic": wilcoxon_stat, "p-value": wilcoxon_p}
#
# Levene's Test for equality of variances
# levene_stat, levene_p = levene(data[f'{var1}{score_suffix}'], data[f'{var2}{score_suffix}'])
# pairwise_results['Levene\'s Test'][pair_name_score] = {"Statistic": levene_stat, "p-value": levene_p}
# T-test for independent samples
t_stat, t_p = ttest_ind(data[f'{var1}{score_suffix}'], data[f'{var2}{score_suffix}'])
#equal_var=(levene_p > 0.05))
pairwise_results['T-Test'][pair_name_score] = {"Statistic": t_stat, "p-value": t_p}
# ANOVA and post-hoc tests if applicable
# score_columns = [v + score_suffix for v in variables]
# score_data = [data[col] for col in score_columns]
# anova_stat, anova_p = f_oneway(*score_data)
# if anova_p < 0.05:
# mc = MultiComparison(data.melt()['value'], data.melt()['variable'])
# tukey_result = mc.tukeyhsd()
# tukey_result_summary = tukey_result.summary().as_html()
# else:
# tukey_result_summary = "ANOVA not significant, no post-hoc test performed."
results = {
#"Average Ranks": average_ranks.to_dict(),
"Friedman Test": {
"Statistic": friedmanchisquare(*rank_data).statistic,
"p-value": friedmanchisquare(*rank_data).pvalue
},
# "Kruskal-Wallis Test": {"Statistic": kw_stat, "p-value": kw_p},
# **pairwise_results,
# "ANOVA Test": {"Statistic": anova_stat, "p-value": anova_p},
#"Tukey HSD Test": tukey_result_summary
}
return results |