532096f9f9a96f689ee644cdb3560def98a4c410,auto_ml/predictor.py,Predictor,_get_xgb_feat_importances,#Predictor#Any#,279

Before Change

        fscore_list = [[int(k[1:]), v] for k, v in fscore.viewitems()]
        print("fscore_list")
        print(fscore_list)
        sorted_fscore = fscore_list.sort(key=lambda x: x[0])
        print("fscore_list after sorting")
        print(fscore_list)

        print("len(fscore_list)")

After Change

        fscore_list = [[int(k[1:]), v] for k, v in fscore.viewitems()]


        feature_infos = []
        sum_of_all_feature_importances = 0.0

        for idx_and_result in fscore_list:
            idx = idx_and_result[0]
            // Use the index that we grabbed above to find the human-readable feature name
            feature_name = trained_feature_names[idx]
            feat_importance = idx_and_result[1]

            // If we sum up all the feature importances and then divide by that sum, we will be able to have each feature importance as it"s relative feature imoprtance, and the sum of all of them will sum up to 1, just as it is in scikit-learn.
            sum_of_all_feature_importances += feat_importance
            feature_infos.append([feature_name, feat_importance])

        sorted_feature_infos = sorted(feature_infos, key=lambda x: x[1])

        print("Here are the feature_importances from the tree-based model:")
        print("The printed list will only contain at most the top 50 features.")

Instances