Max on the wrong axis in _reduce_vars_sklearn

[AndreaVidali]

Hello, I think I found a possible bug in src/arfs/feature_selection/allrelevant.py, in function _reduce_vars_sklearn:

    # Get mean value from the shadows (max, like in Boruta, median to mitigate variance)
    mean_shadow = (
        shadow_vars.select_dtypes(include=[np.number])
        .max(skipna=True)
        .mean(skipna=True)
        / cutoff
    )

The bug is about max, which should have axis=1. Without it, It takes the maximum shap of all features, for each iteration. But I would expect the contrary, which is the maximum shap of all iteration, for each feature.

I may be wrong here, but I had to ask. Thank you!

Side note: probably that comment above the function is outdated :)

[ThomasBury]

Hi @AndreaVidali,

Thank you for your thorough review; I appreciate it. You're correct, the axis=1 argument is missing.That will be corrected in the next release.

In the original version, .mean(axis=1).mean() was used. I opted for a more aggressive approach with .max(axis=1).mean() in the current iteration.

[ThomasBury]

Solved by 945d8fc

[AndreaVidali]

Thanks @ThomasBury I am glad to help!