fix: impute NaN before variable-selection steps in stats.py#313
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drussellmrichie wants to merge 1 commit intolarsiusprime:masterfrom
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fix: impute NaN before variable-selection steps in stats.py#313drussellmrichie wants to merge 1 commit intolarsiusprime:masterfrom
drussellmrichie wants to merge 1 commit intolarsiusprime:masterfrom
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sklearn (ElasticNet) and statsmodels (OLS/VIF) raise errors when input features contain NaN values. This is triggered in practice when a dataset uses LightGBM's native NaN-handling (e.g. sparse binary indicators like "has_garage" or "has_fireplace" where NaN means "not recorded") and runs the variable-selection pre-pass before LightGBM training. The fix adds median imputation of NaN to the top of each of the four variable-selection functions: - calc_elastic_net_regularization - calc_p_values_recursive_drop - calc_t_values_recursive_drop - calc_vif_recursive_drop Imputation is scoped to these pre-passes only: LightGBM training still receives the real NaN values and handles them natively at each split. A UserWarning is emitted listing the affected columns so the user are aware. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Problem
sklearn (
ElasticNet) and statsmodels (OLS/VIF) raise errors when input features containNaNvalues. This is triggered when a dataset uses LightGBM native NaN-handling -- e.g. sparse binary indicators likehas_garagewhereNaNmeans "not recorded" -- and the variable-selection pre-pass runs before LightGBM training.Errors seen:
ValueError: Input X contains NaN(ElasticNet / sklearn)MissingDataError: exog contains inf or nans(statsmodels OLS)Fix
Add median imputation of
NaNto the top of each of the four variable-selection functions:calc_elastic_net_regularizationcalc_p_values_recursive_dropcalc_t_values_recursive_dropcalc_vif_recursive_dropImputation is scoped to these pre-passes only: LightGBM training still receives the real
NaNvalues and handles them natively at each split. AUserWarningis emitted listing the affected columns. Median imputation is a neutral choice for a variable-selection screen and does not bias which variables survive the screen.