We found a small inconsistency - in plot_univariate_predictor_quality() we sort by 'AUC train'
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df = (df_auc[df_auc["preselection"]] |
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.sort_values(by='AUC train', ascending=False)) |
while in compute_univariate_preselection() we sort by 'AUC selection'.
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return (df_auc.sort_values(by='AUC selection', ascending=False) |
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.reset_index(drop=True)) |
It does not ahve any effect on the modeling, but then the plot and preselected_predictors have different order of variables, which is confusing.
Both should be sorted by 'AUC selection'.
We found a small inconsistency - in plot_univariate_predictor_quality() we sort by 'AUC train'
cobra/cobra/evaluation/plotting_utils.py
Lines 26 to 27 in 0133435
while in compute_univariate_preselection() we sort by 'AUC selection'.
cobra/cobra/model_building/univariate_selection.py
Lines 91 to 92 in 0133435
It does not ahve any effect on the modeling, but then the plot and preselected_predictors have different order of variables, which is confusing.
Both should be sorted by 'AUC selection'.