DivRank mitigation algorithm

To replicate the results, users can launch A_00* scripts.

A_001, A_002 and A_003 executes the re-ranking algorithms for all the analyzed datasets.

• A_001 runs the mitigation with Div-rank
• A_002 runs the mitigation with Feldman
• A_003 runs the mitigation with CFA-\theta
• A_004 runs the sensitivity analysis of Div-rank
• A_005 runs the additional test with div-rank and all attributes as input
• A_006 runs the additional test with CFA$theta$ and all attributes as input

The notebook 'B_collect_results' collect the results of A_001 to A_004.

The notebook 'B_01_collect_results' collect the results of A_001 to A_004.

The notebook 'B_02_all_attributes_collect_results' collect the results of A_005 and A_006 and runs the additional test with Feldman and all attributes as input

How to apply DivRank

Let df be our dataset, with 'score' column as our utility.

    from divrank_mitigation import Mitigation


    mit_obj = Mitigation()
    all_results, df_mitigated = mit_obj.iterative_mitigate_minmax(
        df,
        min_sup=0.01,
        target='score',
    )

df_mitigated contains the adjusted score, after the mitigation.

all_results contains a summary of the entire process of mitigation:

We can inspect the change in the Global Shapley value

from utils_mitigation import get_fpdiv
from utils_plot import compareShapleyValues


fpa = get_fpdiv(df_selection, 'score', min_sup = 0.01)
fpb = get_fpdiv(df_mit_score, 'score', min_sup = 0.01)



compareShapleyValues(fpa.computeGlobalShapleyValue(),\
                    fpb.computeGlobalShapleyValue(), \
                    title=[f"original", f"mitigated"],\
                    sizeFig=(4.5,3), shared_x=True);