SFS with unfixed k_features for regression, which scoring includes penalty of dimension?

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When use step function in R, the scoring criteria is AIC, so the penalty of dimension is part of the scoring. But when I use python, I don't know which scoring I should choose, especially when 'k_features' is not fixed.

When I use 'k_features' = (1,8), the result of sfs.k_feature_idx_ is always just (0,)

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Can anyone tell me how to fix this?

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