Bugfix/bias mitigation #8
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| @@ -65,7 +64,7 @@ def _one_vs_all_model_metric( | |||
| Name of the base metric to be called. | |||
| distance_measure : str or None | |||
| Determines the distance used to compare a subgroup's metric | |||
| against the rest of the population. Possible values are: | |||
| against the rest of subgroups. Possible values are: | |||
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| against the rest of subgroups. Possible values are: | |
| against the rest of the subgroups. Possible values are: |
Here and elsewhere
| for group, metric in zip(groups, metrics): | ||
| res[group] += [metric] | ||
| for group in res: | ||
| res[group] = sum(res[group])/len(res[group]) |
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This doesn't look quite right. Are you calculating the mean disparity for all pairs that contain group?
For the raw reduction, we are expecting to see all of the group pairs and the disparity between them.
E.g., {(g1, g2): g1_g2_disparity, (g1, g3): g1_g3_disparity, (g2,g3): g2_g3_disparity}
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Oh, I did average based on None reduction definition on docs and the groups parameter which was only unpriv_group in call(self, groups, metrics)
Now, I changed groups to be subgroup_pairs [(g1, g2), ...], and fixed the reduction to return {(g1,g2): metric, ...}
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Hi @ehsan-s , could you please pull the change from the main branch? There are some updates to fix the failed |
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@YashaPushak Some new changes on _dataset_metric function is applied, please review again! |
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Two minor changes in bias mitigation: