From a7fd7220efca641f43e15dc01d6cc6a4b89359ef Mon Sep 17 00:00:00 2001 From: zyliang2001 Date: Sun, 12 May 2024 15:33:43 -0700 Subject: [PATCH] add competing_methods_local.py --- .../real_data_ablation_visulization_new.ipynb | 16451 ++++++++++++++-- .../scripts/competing_methods_local.py | 551 + 2 files changed, 15069 insertions(+), 1933 deletions(-) create mode 100644 feature_importance/scripts/competing_methods_local.py diff --git a/feature_importance/real_data_ablation_visulization_new.ipynb b/feature_importance/real_data_ablation_visulization_new.ipynb index 128ea01..5de3aa4 100644 --- a/feature_importance/real_data_ablation_visulization_new.ipynb +++ b/feature_importance/real_data_ablation_visulization_new.ipynb @@ -21,7 +21,7 @@ "source": [ "# directory = './results/mdi_local.real_data_regression/diabetes_regression_parallel/varying_sample_row_n/'\n", "# directory = './results/mdi_local.real_data_classification/diabetes_classification_parallel/varying_sample_row_n/'\n", - "directory = './results/mdi_local.real_data_regression/diabetes_regression_parallel/varying_sample_row_n'\n", + "directory = './results/mdi_local.real_data_regression/diabetes_regr/varying_sample_row_n'\n", "folder_names = [folder for folder in os.listdir(directory) if os.path.isdir(os.path.join(directory, folder))]\n", "experiments_seeds = []\n", "for folder_name in folder_names:\n", @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -78,159 +78,578 @@ " n_estimators\n", " min_samples_leaf\n", " max_features\n", - " cv_ridge\n", - " calc_loo_coef\n", + " random_state\n", " include_raw\n", - " sample_split\n", " fit_on\n", " model\n", " fi\n", - " splitting_strategy\n", " train_size\n", + " test_size\n", " num_features\n", " data_split_seed\n", - " test_size\n", - " test_all_mse\n", - " test_all_r2\n", + " test_all_mse_rf\n", + " test_all_r2_rf\n", + " test_all_mse_rf_plus\n", + " test_all_r2_rf_plus\n", + " sample_train_0\n", " sample_test_0\n", + " sample_train_1\n", " sample_test_1\n", + " sample_train_2\n", " sample_test_2\n", + " sample_train_3\n", " sample_test_3\n", + " sample_train_4\n", " sample_test_4\n", + " sample_train_5\n", " sample_test_5\n", + " sample_train_6\n", " sample_test_6\n", + " sample_train_7\n", " sample_test_7\n", + " sample_train_8\n", " sample_test_8\n", + " sample_train_9\n", " sample_test_9\n", + " sample_train_10\n", " sample_test_10\n", + " sample_train_11\n", " sample_test_11\n", + " sample_train_12\n", " sample_test_12\n", + " sample_train_13\n", " sample_test_13\n", + " sample_train_14\n", " sample_test_14\n", + " sample_train_15\n", " sample_test_15\n", + " sample_train_16\n", " sample_test_16\n", + " sample_train_17\n", " sample_test_17\n", + " sample_train_18\n", " sample_test_18\n", + " sample_train_19\n", " sample_test_19\n", + " sample_train_20\n", " sample_test_20\n", + " sample_train_21\n", " sample_test_21\n", + " sample_train_22\n", " sample_test_22\n", + " sample_train_23\n", " sample_test_23\n", + " sample_train_24\n", " sample_test_24\n", + " sample_train_25\n", " sample_test_25\n", + " sample_train_26\n", " sample_test_26\n", + " sample_train_27\n", " sample_test_27\n", + " sample_train_28\n", " sample_test_28\n", + " sample_train_29\n", " sample_test_29\n", + " sample_train_30\n", " sample_test_30\n", + " sample_train_31\n", " sample_test_31\n", + " sample_train_32\n", " sample_test_32\n", + " sample_train_33\n", " sample_test_33\n", + " sample_train_34\n", " sample_test_34\n", + " sample_train_35\n", " sample_test_35\n", + " sample_train_36\n", " sample_test_36\n", + " sample_train_37\n", " sample_test_37\n", + " sample_train_38\n", " sample_test_38\n", + " sample_train_39\n", " sample_test_39\n", + " sample_train_40\n", " sample_test_40\n", + " sample_train_41\n", " sample_test_41\n", + " sample_train_42\n", " sample_test_42\n", + " sample_train_43\n", " sample_test_43\n", + " sample_train_44\n", " sample_test_44\n", + " sample_train_45\n", " sample_test_45\n", + " sample_train_46\n", " sample_test_46\n", + " sample_train_47\n", " sample_test_47\n", + " sample_train_48\n", " sample_test_48\n", + " sample_train_49\n", " sample_test_49\n", + " sample_train_50\n", " sample_test_50\n", + " sample_train_51\n", " sample_test_51\n", + " sample_train_52\n", " sample_test_52\n", + " sample_train_53\n", " sample_test_53\n", + " sample_train_54\n", " sample_test_54\n", + " sample_train_55\n", " sample_test_55\n", + " sample_train_56\n", " sample_test_56\n", + " sample_train_57\n", " sample_test_57\n", + " sample_train_58\n", " sample_test_58\n", + " sample_train_59\n", " sample_test_59\n", + " sample_train_60\n", " sample_test_60\n", + " sample_train_61\n", " sample_test_61\n", + " sample_train_62\n", " sample_test_62\n", + " sample_train_63\n", " sample_test_63\n", + " sample_train_64\n", " sample_test_64\n", + " sample_train_65\n", " sample_test_65\n", + " sample_train_66\n", " sample_test_66\n", + " sample_train_67\n", " sample_test_67\n", + " sample_train_68\n", " sample_test_68\n", + " sample_train_69\n", " sample_test_69\n", + " sample_train_70\n", " sample_test_70\n", + " sample_train_71\n", " sample_test_71\n", + " sample_train_72\n", " sample_test_72\n", + " sample_train_73\n", " sample_test_73\n", + " sample_train_74\n", " sample_test_74\n", + " sample_train_75\n", " sample_test_75\n", + " sample_train_76\n", " sample_test_76\n", + " sample_train_77\n", " sample_test_77\n", + " sample_train_78\n", " sample_test_78\n", + " sample_train_79\n", " sample_test_79\n", + " sample_train_80\n", " sample_test_80\n", + " sample_train_81\n", " sample_test_81\n", + " sample_train_82\n", " sample_test_82\n", + " sample_train_83\n", " sample_test_83\n", + " sample_train_84\n", " sample_test_84\n", + " sample_train_85\n", " sample_test_85\n", + " sample_train_86\n", " sample_test_86\n", + " sample_train_87\n", " sample_test_87\n", + " sample_train_88\n", " sample_test_88\n", + " sample_train_89\n", " sample_test_89\n", + " sample_train_90\n", " sample_test_90\n", + " sample_train_91\n", " sample_test_91\n", + " sample_train_92\n", " sample_test_92\n", + " sample_train_93\n", " sample_test_93\n", + " sample_train_94\n", " sample_test_94\n", + " sample_train_95\n", " sample_test_95\n", + " sample_train_96\n", " sample_test_96\n", + " sample_train_97\n", " sample_test_97\n", + " sample_train_98\n", " sample_test_98\n", + " sample_train_99\n", " sample_test_99\n", " ablation_seed_0\n", - " ablation_seed_1\n", - " ablation_seed_2\n", - " ablation_seed_3\n", - " ablation_seed_4\n", - " ablation_seed_5\n", - " ablation_seed_6\n", - " ablation_seed_7\n", - " ablation_seed_8\n", - " ablation_seed_9\n", " fi_time\n", - " MSE_before_ablation\n", - " R_2_before_ablation\n", - " MSE_after_ablation_1\n", - " R_2_after_ablation_1\n", - " MSE_after_ablation_2\n", - " R_2_after_ablation_2\n", - " MSE_after_ablation_3\n", - " R_2_after_ablation_3\n", - " MSE_after_ablation_4\n", - " R_2_after_ablation_4\n", - " MSE_after_ablation_5\n", - " R_2_after_ablation_5\n", - " MSE_after_ablation_6\n", - " R_2_after_ablation_6\n", - " MSE_after_ablation_7\n", - " R_2_after_ablation_7\n", - " MSE_after_ablation_8\n", - " R_2_after_ablation_8\n", - " MSE_after_ablation_9\n", - " R_2_after_ablation_9\n", - " MSE_after_ablation_10\n", - " R_2_after_ablation_10\n", - " ablation_time\n", + " RF_Regressor_train_subset_MSE_before_ablation\n", + " RF_Regressor_train_subset_R_2_before_ablation\n", + " RF_Regressor_train_subset_MSE_after_ablation_1\n", + " RF_Regressor_train_subset_R_2_after_ablation_1\n", + " RF_Regressor_train_subset_MSE_after_ablation_2\n", + " RF_Regressor_train_subset_R_2_after_ablation_2\n", + " RF_Regressor_train_subset_MSE_after_ablation_3\n", + " RF_Regressor_train_subset_R_2_after_ablation_3\n", + " RF_Regressor_train_subset_MSE_after_ablation_4\n", + " RF_Regressor_train_subset_R_2_after_ablation_4\n", + " RF_Regressor_train_subset_MSE_after_ablation_5\n", + " RF_Regressor_train_subset_R_2_after_ablation_5\n", + " RF_Regressor_train_subset_MSE_after_ablation_6\n", + " RF_Regressor_train_subset_R_2_after_ablation_6\n", + " RF_Regressor_train_subset_MSE_after_ablation_7\n", + " RF_Regressor_train_subset_R_2_after_ablation_7\n", + " RF_Regressor_train_subset_MSE_after_ablation_8\n", + " RF_Regressor_train_subset_R_2_after_ablation_8\n", + " RF_Regressor_train_subset_MSE_after_ablation_9\n", + " RF_Regressor_train_subset_R_2_after_ablation_9\n", + " RF_Regressor_train_subset_MSE_after_ablation_10\n", + " RF_Regressor_train_subset_R_2_after_ablation_10\n", + " Linear_train_subset_MSE_before_ablation\n", + " Linear_train_subset_R_2_before_ablation\n", + " Linear_train_subset_MSE_after_ablation_1\n", + " Linear_train_subset_R_2_after_ablation_1\n", + " Linear_train_subset_MSE_after_ablation_2\n", + " Linear_train_subset_R_2_after_ablation_2\n", + " Linear_train_subset_MSE_after_ablation_3\n", + " Linear_train_subset_R_2_after_ablation_3\n", + " Linear_train_subset_MSE_after_ablation_4\n", + " Linear_train_subset_R_2_after_ablation_4\n", + " Linear_train_subset_MSE_after_ablation_5\n", + " Linear_train_subset_R_2_after_ablation_5\n", + " Linear_train_subset_MSE_after_ablation_6\n", + " Linear_train_subset_R_2_after_ablation_6\n", + " Linear_train_subset_MSE_after_ablation_7\n", + " Linear_train_subset_R_2_after_ablation_7\n", + " Linear_train_subset_MSE_after_ablation_8\n", + " Linear_train_subset_R_2_after_ablation_8\n", + " Linear_train_subset_MSE_after_ablation_9\n", + " Linear_train_subset_R_2_after_ablation_9\n", + " Linear_train_subset_MSE_after_ablation_10\n", + " Linear_train_subset_R_2_after_ablation_10\n", + " XGB_Regressor_train_subset_MSE_before_ablation\n", + " XGB_Regressor_train_subset_R_2_before_ablation\n", + " XGB_Regressor_train_subset_MSE_after_ablation_1\n", + " XGB_Regressor_train_subset_R_2_after_ablation_1\n", + " XGB_Regressor_train_subset_MSE_after_ablation_2\n", + " XGB_Regressor_train_subset_R_2_after_ablation_2\n", + " XGB_Regressor_train_subset_MSE_after_ablation_3\n", + " XGB_Regressor_train_subset_R_2_after_ablation_3\n", + " XGB_Regressor_train_subset_MSE_after_ablation_4\n", + " XGB_Regressor_train_subset_R_2_after_ablation_4\n", + " XGB_Regressor_train_subset_MSE_after_ablation_5\n", + " XGB_Regressor_train_subset_R_2_after_ablation_5\n", + " XGB_Regressor_train_subset_MSE_after_ablation_6\n", + " XGB_Regressor_train_subset_R_2_after_ablation_6\n", + " XGB_Regressor_train_subset_MSE_after_ablation_7\n", + " XGB_Regressor_train_subset_R_2_after_ablation_7\n", + " XGB_Regressor_train_subset_MSE_after_ablation_8\n", + " XGB_Regressor_train_subset_R_2_after_ablation_8\n", + " XGB_Regressor_train_subset_MSE_after_ablation_9\n", + " XGB_Regressor_train_subset_R_2_after_ablation_9\n", + " XGB_Regressor_train_subset_MSE_after_ablation_10\n", + " XGB_Regressor_train_subset_R_2_after_ablation_10\n", + " RF_Plus_Regressor_train_subset_MSE_before_ablation\n", + " RF_Plus_Regressor_train_subset_R_2_before_ablation\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_1\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_1\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_2\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_2\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_3\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_3\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_4\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_4\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_5\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_5\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_6\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_6\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_7\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_7\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_8\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_8\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_9\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_9\n", + " RF_Plus_Regressor_train_subset_MSE_after_ablation_10\n", + " RF_Plus_Regressor_train_subset_R_2_after_ablation_10\n", + " train_subset_ablation_time\n", + " RF_Regressor_test_subset_MSE_before_ablation\n", + " RF_Regressor_test_subset_R_2_before_ablation\n", + " RF_Regressor_test_subset_MSE_after_ablation_1\n", + " RF_Regressor_test_subset_R_2_after_ablation_1\n", + " RF_Regressor_test_subset_MSE_after_ablation_2\n", + " RF_Regressor_test_subset_R_2_after_ablation_2\n", + " RF_Regressor_test_subset_MSE_after_ablation_3\n", + " RF_Regressor_test_subset_R_2_after_ablation_3\n", + " RF_Regressor_test_subset_MSE_after_ablation_4\n", + " RF_Regressor_test_subset_R_2_after_ablation_4\n", + " RF_Regressor_test_subset_MSE_after_ablation_5\n", + " RF_Regressor_test_subset_R_2_after_ablation_5\n", + " RF_Regressor_test_subset_MSE_after_ablation_6\n", + " RF_Regressor_test_subset_R_2_after_ablation_6\n", + " RF_Regressor_test_subset_MSE_after_ablation_7\n", + " RF_Regressor_test_subset_R_2_after_ablation_7\n", + " RF_Regressor_test_subset_MSE_after_ablation_8\n", + " RF_Regressor_test_subset_R_2_after_ablation_8\n", + " RF_Regressor_test_subset_MSE_after_ablation_9\n", + " RF_Regressor_test_subset_R_2_after_ablation_9\n", + " RF_Regressor_test_subset_MSE_after_ablation_10\n", + " RF_Regressor_test_subset_R_2_after_ablation_10\n", + " Linear_test_subset_MSE_before_ablation\n", + " Linear_test_subset_R_2_before_ablation\n", + " Linear_test_subset_MSE_after_ablation_1\n", + " Linear_test_subset_R_2_after_ablation_1\n", + " Linear_test_subset_MSE_after_ablation_2\n", + " Linear_test_subset_R_2_after_ablation_2\n", + " Linear_test_subset_MSE_after_ablation_3\n", + " Linear_test_subset_R_2_after_ablation_3\n", + " Linear_test_subset_MSE_after_ablation_4\n", + " Linear_test_subset_R_2_after_ablation_4\n", + " Linear_test_subset_MSE_after_ablation_5\n", + " Linear_test_subset_R_2_after_ablation_5\n", + " Linear_test_subset_MSE_after_ablation_6\n", + " Linear_test_subset_R_2_after_ablation_6\n", + " Linear_test_subset_MSE_after_ablation_7\n", + " Linear_test_subset_R_2_after_ablation_7\n", + " Linear_test_subset_MSE_after_ablation_8\n", + " Linear_test_subset_R_2_after_ablation_8\n", + " Linear_test_subset_MSE_after_ablation_9\n", + " Linear_test_subset_R_2_after_ablation_9\n", + " Linear_test_subset_MSE_after_ablation_10\n", + " Linear_test_subset_R_2_after_ablation_10\n", + " XGB_Regressor_test_subset_MSE_before_ablation\n", + " XGB_Regressor_test_subset_R_2_before_ablation\n", + " XGB_Regressor_test_subset_MSE_after_ablation_1\n", + " XGB_Regressor_test_subset_R_2_after_ablation_1\n", + " XGB_Regressor_test_subset_MSE_after_ablation_2\n", + " XGB_Regressor_test_subset_R_2_after_ablation_2\n", + " XGB_Regressor_test_subset_MSE_after_ablation_3\n", + " XGB_Regressor_test_subset_R_2_after_ablation_3\n", + " XGB_Regressor_test_subset_MSE_after_ablation_4\n", + " XGB_Regressor_test_subset_R_2_after_ablation_4\n", + " XGB_Regressor_test_subset_MSE_after_ablation_5\n", + " XGB_Regressor_test_subset_R_2_after_ablation_5\n", + " XGB_Regressor_test_subset_MSE_after_ablation_6\n", + " XGB_Regressor_test_subset_R_2_after_ablation_6\n", + " XGB_Regressor_test_subset_MSE_after_ablation_7\n", + " XGB_Regressor_test_subset_R_2_after_ablation_7\n", + " XGB_Regressor_test_subset_MSE_after_ablation_8\n", + " XGB_Regressor_test_subset_R_2_after_ablation_8\n", + " XGB_Regressor_test_subset_MSE_after_ablation_9\n", + " XGB_Regressor_test_subset_R_2_after_ablation_9\n", + " XGB_Regressor_test_subset_MSE_after_ablation_10\n", + " XGB_Regressor_test_subset_R_2_after_ablation_10\n", + " RF_Plus_Regressor_test_subset_MSE_before_ablation\n", + " RF_Plus_Regressor_test_subset_R_2_before_ablation\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_1\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_1\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_2\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_2\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_3\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_3\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_4\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_4\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_5\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_5\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_6\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_6\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_7\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_7\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_8\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_8\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_9\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_9\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_10\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_10\n", + " test_subset_ablation_time\n", + " RF_Regressor_test_subset_MSE_before_ablation_blank\n", + " RF_Regressor_test_subset_R_2_before_ablation_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_1_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_1_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_2_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_2_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_3_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_3_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_4_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_4_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_5_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_5_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_6_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_6_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_7_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_7_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_8_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_8_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_9_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_9_blank\n", + " RF_Regressor_test_subset_MSE_after_ablation_10_blank\n", + " RF_Regressor_test_subset_R_2_after_ablation_10_blank\n", + " Linear_test_subset_MSE_before_ablation_blank\n", + " Linear_test_subset_R_2_before_ablation_blank\n", + " Linear_test_subset_MSE_after_ablation_1_blank\n", + " Linear_test_subset_R_2_after_ablation_1_blank\n", + " Linear_test_subset_MSE_after_ablation_2_blank\n", + " Linear_test_subset_R_2_after_ablation_2_blank\n", + " Linear_test_subset_MSE_after_ablation_3_blank\n", + " Linear_test_subset_R_2_after_ablation_3_blank\n", + " Linear_test_subset_MSE_after_ablation_4_blank\n", + " Linear_test_subset_R_2_after_ablation_4_blank\n", + " Linear_test_subset_MSE_after_ablation_5_blank\n", + " Linear_test_subset_R_2_after_ablation_5_blank\n", + " Linear_test_subset_MSE_after_ablation_6_blank\n", + " Linear_test_subset_R_2_after_ablation_6_blank\n", + " Linear_test_subset_MSE_after_ablation_7_blank\n", + " Linear_test_subset_R_2_after_ablation_7_blank\n", + " Linear_test_subset_MSE_after_ablation_8_blank\n", + " Linear_test_subset_R_2_after_ablation_8_blank\n", + " Linear_test_subset_MSE_after_ablation_9_blank\n", + " Linear_test_subset_R_2_after_ablation_9_blank\n", + " Linear_test_subset_MSE_after_ablation_10_blank\n", + " Linear_test_subset_R_2_after_ablation_10_blank\n", + " XGB_Regressor_test_subset_MSE_before_ablation_blank\n", + " XGB_Regressor_test_subset_R_2_before_ablation_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_1_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_1_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_2_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_2_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_3_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_3_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_4_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_4_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_5_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_5_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_6_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_6_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_7_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_7_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_8_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_8_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_9_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_9_blank\n", + " XGB_Regressor_test_subset_MSE_after_ablation_10_blank\n", + " XGB_Regressor_test_subset_R_2_after_ablation_10_blank\n", + " RF_Plus_Regressor_test_subset_MSE_before_ablation_blank\n", + " RF_Plus_Regressor_test_subset_R_2_before_ablation_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_1_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_1_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_2_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_2_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_3_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_3_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_4_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_4_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_5_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_5_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_6_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_6_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_7_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_7_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_8_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_8_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_9_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_9_blank\n", + " RF_Plus_Regressor_test_subset_MSE_after_ablation_10_blank\n", + " RF_Plus_Regressor_test_subset_R_2_after_ablation_10_blank\n", + " test_subset_blank_ablation_time\n", + " RF_Regressor_test_MSE_before_ablation\n", + " RF_Regressor_test_R_2_before_ablation\n", + " RF_Regressor_test_MSE_after_ablation_1\n", + " RF_Regressor_test_R_2_after_ablation_1\n", + " RF_Regressor_test_MSE_after_ablation_2\n", + " RF_Regressor_test_R_2_after_ablation_2\n", + " RF_Regressor_test_MSE_after_ablation_3\n", + " RF_Regressor_test_R_2_after_ablation_3\n", + " RF_Regressor_test_MSE_after_ablation_4\n", + " RF_Regressor_test_R_2_after_ablation_4\n", + " RF_Regressor_test_MSE_after_ablation_5\n", + " RF_Regressor_test_R_2_after_ablation_5\n", + " RF_Regressor_test_MSE_after_ablation_6\n", + " RF_Regressor_test_R_2_after_ablation_6\n", + " RF_Regressor_test_MSE_after_ablation_7\n", + " RF_Regressor_test_R_2_after_ablation_7\n", + " RF_Regressor_test_MSE_after_ablation_8\n", + " RF_Regressor_test_R_2_after_ablation_8\n", + " RF_Regressor_test_MSE_after_ablation_9\n", + " RF_Regressor_test_R_2_after_ablation_9\n", + " RF_Regressor_test_MSE_after_ablation_10\n", + " RF_Regressor_test_R_2_after_ablation_10\n", + " Linear_test_MSE_before_ablation\n", + " Linear_test_R_2_before_ablation\n", + " Linear_test_MSE_after_ablation_1\n", + " Linear_test_R_2_after_ablation_1\n", + " Linear_test_MSE_after_ablation_2\n", + " Linear_test_R_2_after_ablation_2\n", + " Linear_test_MSE_after_ablation_3\n", + " Linear_test_R_2_after_ablation_3\n", + " Linear_test_MSE_after_ablation_4\n", + " Linear_test_R_2_after_ablation_4\n", + " Linear_test_MSE_after_ablation_5\n", + " Linear_test_R_2_after_ablation_5\n", + " Linear_test_MSE_after_ablation_6\n", + " Linear_test_R_2_after_ablation_6\n", + " Linear_test_MSE_after_ablation_7\n", + " Linear_test_R_2_after_ablation_7\n", + " Linear_test_MSE_after_ablation_8\n", + " Linear_test_R_2_after_ablation_8\n", + " Linear_test_MSE_after_ablation_9\n", + " Linear_test_R_2_after_ablation_9\n", + " Linear_test_MSE_after_ablation_10\n", + " Linear_test_R_2_after_ablation_10\n", + " XGB_Regressor_test_MSE_before_ablation\n", + " XGB_Regressor_test_R_2_before_ablation\n", + " XGB_Regressor_test_MSE_after_ablation_1\n", + " XGB_Regressor_test_R_2_after_ablation_1\n", + " XGB_Regressor_test_MSE_after_ablation_2\n", + " XGB_Regressor_test_R_2_after_ablation_2\n", + " XGB_Regressor_test_MSE_after_ablation_3\n", + " XGB_Regressor_test_R_2_after_ablation_3\n", + " XGB_Regressor_test_MSE_after_ablation_4\n", + " XGB_Regressor_test_R_2_after_ablation_4\n", + " XGB_Regressor_test_MSE_after_ablation_5\n", + " XGB_Regressor_test_R_2_after_ablation_5\n", + " XGB_Regressor_test_MSE_after_ablation_6\n", + " XGB_Regressor_test_R_2_after_ablation_6\n", + " XGB_Regressor_test_MSE_after_ablation_7\n", + " XGB_Regressor_test_R_2_after_ablation_7\n", + " XGB_Regressor_test_MSE_after_ablation_8\n", + " XGB_Regressor_test_R_2_after_ablation_8\n", + " XGB_Regressor_test_MSE_after_ablation_9\n", + " XGB_Regressor_test_R_2_after_ablation_9\n", + " XGB_Regressor_test_MSE_after_ablation_10\n", + " XGB_Regressor_test_R_2_after_ablation_10\n", + " RF_Plus_Regressor_test_MSE_before_ablation\n", + " RF_Plus_Regressor_test_R_2_before_ablation\n", + " RF_Plus_Regressor_test_MSE_after_ablation_1\n", + " RF_Plus_Regressor_test_R_2_after_ablation_1\n", + " RF_Plus_Regressor_test_MSE_after_ablation_2\n", + " RF_Plus_Regressor_test_R_2_after_ablation_2\n", + " RF_Plus_Regressor_test_MSE_after_ablation_3\n", + " RF_Plus_Regressor_test_R_2_after_ablation_3\n", + " RF_Plus_Regressor_test_MSE_after_ablation_4\n", + " RF_Plus_Regressor_test_R_2_after_ablation_4\n", + " RF_Plus_Regressor_test_MSE_after_ablation_5\n", + " RF_Plus_Regressor_test_R_2_after_ablation_5\n", + " RF_Plus_Regressor_test_MSE_after_ablation_6\n", + " RF_Plus_Regressor_test_R_2_after_ablation_6\n", + " RF_Plus_Regressor_test_MSE_after_ablation_7\n", + " RF_Plus_Regressor_test_R_2_after_ablation_7\n", + " RF_Plus_Regressor_test_MSE_after_ablation_8\n", + " RF_Plus_Regressor_test_R_2_after_ablation_8\n", + " RF_Plus_Regressor_test_MSE_after_ablation_9\n", + " RF_Plus_Regressor_test_R_2_after_ablation_9\n", + " RF_Plus_Regressor_test_MSE_after_ablation_10\n", + " RF_Plus_Regressor_test_R_2_after_ablation_10\n", + " test_data_ablation_time\n", " split_seed\n", - " rf_model\n", - " index\n", - " var\n", - " true_support\n", " \n", " \n", " \n", @@ -239,29 +658,491 @@ " NaN\n", " keep_all_rows\n", " 0\n", - " 100.0\n", - " 5.0\n", + " 100\n", + " 5\n", " 0.33\n", - " 5.0\n", - " False\n", - " NaN\n", + " 42\n", " NaN\n", " NaN\n", " RF\n", - " LFI_with_raw_CV_RF\n", - " train-test\n", + " Kernel_SHAP_RF_plus\n", " 296\n", + " 146\n", + " 10\n", + " 1\n", + " 3200.179236\n", + " 0.361008\n", + " 2964.400000\n", + " 0.408087\n", + " 274\n", + " 69\n", + " 155\n", + " 30\n", + " 84\n", + " 39\n", + " 82\n", + " 2\n", + " 261\n", + " 124\n", + " 9\n", + " 10\n", + " 42\n", + " 68\n", + " 277\n", + " 51\n", + " 282\n", + " 71\n", + " 92\n", + " 77\n", + " 148\n", + " 102\n", + " 211\n", + " 80\n", + " 60\n", + " 76\n", + " 218\n", + " 142\n", + " 262\n", + " 127\n", + " 46\n", + " 95\n", + " 45\n", + " 70\n", + " 236\n", + " 93\n", + " 228\n", + " 67\n", + " 132\n", + " 0\n", + " 143\n", + " 105\n", + " 167\n", + " 82\n", + " 152\n", + " 136\n", + " 93\n", + " 40\n", + " 113\n", + " 54\n", + " 5\n", + " 28\n", + 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NaN NaN \n", - "76 NaN NaN NaN NaN \n", - "77 20.0 104.0 47.0 123.0 \n", - "78 20.0 104.0 47.0 123.0 \n", - "79 20.0 104.0 47.0 123.0 \n", - "\n", - " sample_test_74 sample_test_75 sample_test_76 sample_test_77 \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - ".. ... ... ... ... \n", - "75 NaN NaN NaN NaN \n", - "76 NaN NaN NaN NaN \n", - "77 76.0 125.0 95.0 134.0 \n", - "78 76.0 125.0 95.0 134.0 \n", - "79 76.0 125.0 95.0 134.0 \n", - "\n", - " sample_test_78 sample_test_79 sample_test_80 sample_test_81 \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - ".. ... ... ... ... \n", - "75 NaN NaN NaN NaN \n", - "76 NaN NaN NaN NaN \n", - "77 38.0 13.0 129.0 22.0 \n", - "78 38.0 13.0 129.0 22.0 \n", - "79 38.0 13.0 129.0 22.0 \n", - "\n", - " sample_test_82 sample_test_83 sample_test_84 sample_test_85 \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - ".. ... ... ... ... \n", - "75 NaN NaN NaN NaN \n", - "76 NaN NaN NaN NaN \n", - "77 32.0 110.0 62.0 11.0 \n", - "78 32.0 110.0 62.0 11.0 \n", - "79 32.0 110.0 62.0 11.0 \n", - "\n", - " sample_test_86 sample_test_87 sample_test_88 sample_test_89 \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - ".. ... ... ... ... \n", - "75 NaN NaN NaN NaN \n", - "76 NaN NaN NaN NaN \n", - "77 23.0 128.0 65.0 145.0 \n", - "78 23.0 128.0 65.0 145.0 \n", - "79 23.0 128.0 65.0 145.0 \n", - "\n", - " sample_test_90 sample_test_91 sample_test_92 sample_test_93 \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - ".. ... ... ... ... \n", - "75 NaN NaN NaN NaN \n", - "76 NaN NaN NaN NaN \n", - "77 44.0 116.0 39.0 45.0 \n", - "78 44.0 116.0 39.0 45.0 \n", - "79 44.0 116.0 39.0 45.0 \n", - "\n", - " sample_test_94 sample_test_95 sample_test_96 sample_test_97 \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - ".. ... ... ... ... \n", - "75 NaN NaN NaN NaN \n", - "76 NaN NaN NaN NaN \n", - "77 103.0 63.0 18.0 19.0 \n", - "78 103.0 63.0 18.0 19.0 \n", - "79 103.0 63.0 18.0 19.0 \n", - "\n", - " sample_test_98 sample_test_99 ablation_seed_0 ablation_seed_1 \\\n", - "0 NaN NaN 224 4847 \n", - "1 NaN NaN 224 4847 \n", - "2 NaN NaN 224 4847 \n", - "3 NaN NaN 224 4847 \n", - "4 NaN NaN 224 4847 \n", - ".. ... ... ... ... \n", - "75 NaN NaN 3861 146 \n", - "76 NaN NaN 3861 146 \n", - "77 43.0 99.0 6734 8731 \n", - "78 43.0 99.0 6734 8731 \n", - "79 43.0 99.0 6734 8731 \n", - "\n", - " ablation_seed_2 ablation_seed_3 ablation_seed_4 ablation_seed_5 \\\n", - "0 6229 7033 4246 4462 \n", - "1 6229 7033 4246 4462 \n", - "2 6229 7033 4246 4462 \n", - "3 6229 7033 4246 4462 \n", - "4 6229 7033 4246 4462 \n", - ".. ... ... ... ... \n", - "75 5855 1493 3971 5711 \n", - "76 5855 1493 3971 5711 \n", - "77 5921 9043 526 8382 \n", - "78 5921 9043 526 8382 \n", - "79 5921 9043 526 8382 \n", - "\n", - " ablation_seed_6 ablation_seed_7 ablation_seed_8 ablation_seed_9 \\\n", - "0 2467 704 6974 7100 \n", - "1 2467 704 6974 7100 \n", - "2 2467 704 6974 7100 \n", - "3 2467 704 6974 7100 \n", - "4 2467 704 6974 7100 \n", - ".. ... ... ... ... \n", - "75 8760 4156 1273 9581 \n", - "76 8760 4156 1273 9581 \n", - "77 3923 2646 9942 5732 \n", - "78 3923 2646 9942 5732 \n", - "79 3923 2646 9942 5732 \n", - "\n", - " fi_time MSE_before_ablation R_2_before_ablation MSE_after_ablation_1 \\\n", - "0 87.258065 3015.657705 0.493287 4431.464328 \n", - "1 2.991500 3015.657705 0.493287 4403.317011 \n", - "2 3.529108 3015.657705 0.493287 4350.242507 \n", - "3 1.448320 3015.657705 0.493287 4450.729741 \n", - "4 0.166827 3015.657705 0.493287 4425.135054 \n", - ".. ... ... ... ... \n", - "75 0.651751 3364.534109 0.472198 4589.284370 \n", - "76 0.071407 3364.534109 0.472198 4644.131916 \n", - "77 29.754058 3058.202408 0.478782 4223.564334 \n", - "78 0.437314 3058.202408 0.478782 4118.945235 \n", - "79 68.105400 3058.202408 0.478782 4213.920895 \n", - "\n", - " R_2_after_ablation_1 MSE_after_ablation_2 R_2_after_ablation_2 \\\n", - "0 0.255393 5395.476701 0.093413 \n", - "1 0.260123 5510.475704 0.074090 \n", - "2 0.269041 5528.366306 0.071084 \n", - "3 0.252156 5401.335532 0.092429 \n", - "4 0.256457 5571.449691 0.063845 \n", - ".. ... ... ... \n", - "75 0.280069 5510.440643 0.135565 \n", - "76 0.271465 5625.114534 0.117576 \n", - "77 0.280166 5540.213154 0.055766 \n", - "78 0.297996 5522.440952 0.058795 \n", - "79 0.281809 5618.801778 0.042372 \n", - "\n", - " MSE_after_ablation_3 R_2_after_ablation_3 MSE_after_ablation_4 \\\n", - "0 5961.563954 -0.001705 6127.256083 \n", - "1 6062.766174 -0.018710 6420.107254 \n", - "2 6008.104990 -0.009525 6190.662160 \n", - "3 5872.831654 0.013205 6229.387859 \n", - "4 6052.897939 -0.017051 6391.870115 \n", - ".. ... ... ... \n", - "75 6269.197133 0.016537 6802.626607 \n", - "76 6479.673837 -0.016480 7087.133723 \n", - "77 6100.799366 -0.039777 6404.189310 \n", - "78 6139.370419 -0.046350 6561.148099 \n", - "79 6167.544944 -0.051152 6554.712486 \n", - "\n", - " R_2_after_ablation_4 MSE_after_ablation_5 R_2_after_ablation_5 \\\n", - "0 -0.029546 6329.519071 -0.063531 \n", - "1 -0.078753 6550.611119 -0.100681 \n", - "2 -0.040200 6400.736746 -0.075498 \n", - "3 -0.046707 6497.063836 -0.091683 \n", - "4 -0.074008 6662.404069 -0.119465 \n", - ".. ... ... ... \n", - "75 -0.067143 7182.245130 -0.126694 \n", - "76 -0.111774 7414.971343 -0.163203 \n", - "77 -0.091484 6705.799392 -0.142889 \n", - "78 -0.118235 6779.508319 -0.155451 \n", - "79 -0.117138 6770.764955 -0.153961 \n", - "\n", - " MSE_after_ablation_6 R_2_after_ablation_6 MSE_after_ablation_7 \\\n", - "0 6440.261355 -0.082139 6543.210590 \n", - "1 6699.697074 -0.125731 6768.142298 \n", - "2 6540.444309 -0.098973 6621.351976 \n", - "3 6638.438945 -0.115438 6708.991358 \n", - "4 6724.232910 -0.129854 6712.329030 \n", - ".. ... ... ... \n", - "75 7383.240571 -0.158225 7500.322118 \n", - "76 7523.826113 -0.180279 7625.821932 \n", - "77 6918.319441 -0.179109 7039.701464 \n", - "78 6876.299938 -0.171947 6935.213602 \n", - "79 6933.509996 -0.181698 6983.510919 \n", - "\n", - " R_2_after_ablation_7 MSE_after_ablation_8 R_2_after_ablation_8 \\\n", - "0 -0.099437 6598.214822 -0.108680 \n", - "1 -0.137232 6787.349203 -0.140459 \n", - "2 -0.112567 6725.345707 -0.130041 \n", - "3 -0.127293 6692.120871 -0.124458 \n", - "4 -0.127854 6746.616969 -0.133615 \n", - ".. ... ... ... \n", - "75 -0.176592 7560.531068 -0.186037 \n", - "76 -0.196279 7658.318127 -0.201377 \n", - "77 -0.199796 7041.718704 -0.200140 \n", - "78 -0.181988 7002.733329 -0.193496 \n", - "79 -0.190220 7023.012024 -0.196952 \n", - "\n", - " MSE_after_ablation_9 R_2_after_ablation_9 MSE_after_ablation_10 \\\n", - "0 6668.503871 -0.120490 6754.906732 \n", - "1 6725.982194 -0.130148 6754.906732 \n", - "2 6728.526559 -0.130575 6754.906732 \n", - "3 6704.867772 -0.126600 6754.906732 \n", - "4 6754.743785 -0.134981 6754.906732 \n", - ".. ... ... ... \n", - "75 7623.677720 -0.195943 7620.889900 \n", - "76 7654.377608 -0.200759 7620.889900 \n", - "77 7051.753892 -0.201851 7018.716589 \n", - "78 7019.751192 -0.196396 7018.716589 \n", - "79 7014.183445 -0.195447 7018.716589 \n", - "\n", - " R_2_after_ablation_10 ablation_time split_seed \\\n", - "0 -0.135008 2.449821 7 \n", - "1 -0.135008 2.449706 7 \n", - "2 -0.135008 2.461490 7 \n", - "3 -0.135008 2.445521 7 \n", - "4 -0.135008 2.448545 7 \n", - ".. ... ... ... \n", - "75 -0.195506 1.181488 5 \n", - "76 -0.195506 1.182896 5 \n", - "77 -0.196220 23.125059 5 \n", - "78 -0.196220 23.535761 5 \n", - "79 -0.196220 22.972130 5 \n", - "\n", - " rf_model index var \\\n", - "0 NaN 0 0 \n", - "1 NaN 1 0 \n", - "2 NaN 2 0 \n", - "3 NaN 3 0 \n", - "4 NaN 4 0 \n", - ".. ... ... ... \n", - "75 NaN 3 0 \n", - "76 NaN 4 0 \n", - "77 RandomForestRegressor(max_features=0.33, min_s... 5 0 \n", - "78 RandomForestRegressor(max_features=0.33, min_s... 6 0 \n", - "79 RandomForestRegressor(max_features=0.33, min_s... 7 0 \n", - "\n", - " true_support \n", - "0 1.0 \n", - "1 1.0 \n", - "2 1.0 \n", - "3 1.0 \n", - "4 1.0 \n", - ".. ... \n", - "75 1.0 \n", - "76 1.0 \n", - "77 1.0 \n", - "78 1.0 \n", - "79 1.0 \n", - "\n", - "[80 rows x 159 columns]" + "0 NaN keep_all_rows 0 100 5 \n", + "1 NaN keep_all_rows 0 100 5 \n", + "2 NaN keep_all_rows 0 100 5 \n", + "3 NaN keep_all_rows 0 100 5 \n", + "4 NaN keep_all_rows 0 100 5 \n", + "5 NaN keep_all_rows 0 100 5 \n", + "6 NaN keep_all_rows 0 100 5 \n", + "7 NaN keep_all_rows 0 100 5 \n", + "8 NaN keep_all_rows 0 100 5 \n", + "9 NaN keep_all_rows 0 100 5 \n", + "10 NaN keep_all_rows 0 100 5 \n", + "11 NaN keep_all_rows 0 100 5 \n", + "12 NaN keep_all_rows 0 100 5 \n", + "13 NaN keep_all_rows 0 100 5 \n", + "\n", + " max_features random_state include_raw fit_on model \\\n", + "0 0.33 42 NaN NaN RF \n", + "1 0.33 42 NaN NaN RF \n", + "2 0.33 42 NaN NaN RF \n", + "3 0.33 42 NaN oob RF \n", + "4 0.33 42 False inbag RF \n", + "5 0.33 42 NaN NaN RF \n", + "6 0.33 42 NaN NaN RF \n", + "7 0.33 42 NaN NaN RF \n", + "8 0.33 42 NaN NaN RF \n", + "9 0.33 42 NaN NaN RF \n", + "10 0.33 42 NaN oob RF \n", + "11 0.33 42 False inbag RF \n", + "12 0.33 42 NaN NaN RF \n", + "13 0.33 42 NaN NaN RF \n", + "\n", + " fi train_size test_size num_features \\\n", + "0 Kernel_SHAP_RF_plus 296 146 10 \n", + "1 LFI_evaluate_on_all_RF_plus 296 146 10 \n", + "2 LFI_evaluate_on_oob_RF_plus 296 146 10 \n", + "3 LFI_fit_on_OOB_RF 296 146 10 \n", + "4 LFI_fit_on_inbag_RF 296 146 10 \n", + "5 LIME_RF_plus 296 146 10 \n", + "6 TreeSHAP_RF 296 146 10 \n", + "7 Kernel_SHAP_RF_plus 296 146 10 \n", + "8 LFI_evaluate_on_all_RF_plus 296 146 10 \n", + "9 LFI_evaluate_on_oob_RF_plus 296 146 10 \n", + "10 LFI_fit_on_OOB_RF 296 146 10 \n", + "11 LFI_fit_on_inbag_RF 296 146 10 \n", + "12 LIME_RF_plus 296 146 10 \n", + "13 TreeSHAP_RF 296 146 10 \n", + "\n", + " data_split_seed test_all_mse_rf test_all_r2_rf test_all_mse_rf_plus \\\n", + "0 1 3200.179236 0.361008 2964.400000 \n", + "1 1 3200.179236 0.361008 2964.400000 \n", + "2 1 3200.179236 0.361008 2964.400000 \n", + "3 1 3200.179236 0.361008 2964.400000 \n", + "4 1 3200.179236 0.361008 2964.400000 \n", + "5 1 3200.179236 0.361008 2964.400000 \n", + "6 1 3200.179236 0.361008 2964.400000 \n", + "7 2 3437.298393 0.416259 3125.297639 \n", + "8 2 3437.298393 0.416259 3125.297639 \n", + "9 2 3437.298393 0.416259 3125.297639 \n", + "10 2 3437.298393 0.416259 3125.297639 \n", + "11 2 3437.298393 0.416259 3125.297639 \n", + "12 2 3437.298393 0.416259 3125.297639 \n", + "13 2 3437.298393 0.416259 3125.297639 \n", + "\n", + " test_all_r2_rf_plus sample_train_0 sample_test_0 sample_train_1 \\\n", + "0 0.408087 274 69 155 \n", + "1 0.408087 274 69 155 \n", + "2 0.408087 274 69 155 \n", + "3 0.408087 274 69 155 \n", + "4 0.408087 274 69 155 \n", + "5 0.408087 274 69 155 \n", + "6 0.408087 274 69 155 \n", + "7 0.469245 274 69 155 \n", + "8 0.469245 274 69 155 \n", + "9 0.469245 274 69 155 \n", + "10 0.469245 274 69 155 \n", + "11 0.469245 274 69 155 \n", + "12 0.469245 274 69 155 \n", + "13 0.469245 274 69 155 \n", + "\n", + " sample_test_1 sample_train_2 sample_test_2 sample_train_3 \\\n", + "0 30 84 39 82 \n", + "1 30 84 39 82 \n", + "2 30 84 39 82 \n", + "3 30 84 39 82 \n", + "4 30 84 39 82 \n", + "5 30 84 39 82 \n", + "6 30 84 39 82 \n", + "7 30 84 39 82 \n", + "8 30 84 39 82 \n", + "9 30 84 39 82 \n", + "10 30 84 39 82 \n", + "11 30 84 39 82 \n", + "12 30 84 39 82 \n", + "13 30 84 39 82 \n", + "\n", + " sample_test_3 sample_train_4 sample_test_4 sample_train_5 \\\n", + "0 2 261 124 9 \n", + "1 2 261 124 9 \n", + "2 2 261 124 9 \n", + "3 2 261 124 9 \n", + "4 2 261 124 9 \n", + "5 2 261 124 9 \n", + "6 2 261 124 9 \n", + "7 2 261 124 9 \n", + "8 2 261 124 9 \n", + "9 2 261 124 9 \n", + "10 2 261 124 9 \n", + "11 2 261 124 9 \n", + "12 2 261 124 9 \n", + "13 2 261 124 9 \n", + "\n", + " sample_test_5 sample_train_6 sample_test_6 sample_train_7 \\\n", + "0 10 42 68 277 \n", + "1 10 42 68 277 \n", + "2 10 42 68 277 \n", + "3 10 42 68 277 \n", + "4 10 42 68 277 \n", + "5 10 42 68 277 \n", + "6 10 42 68 277 \n", + "7 10 42 68 277 \n", + "8 10 42 68 277 \n", + "9 10 42 68 277 \n", + "10 10 42 68 277 \n", + "11 10 42 68 277 \n", + "12 10 42 68 277 \n", + "13 10 42 68 277 \n", + "\n", + " sample_test_7 sample_train_8 sample_test_8 sample_train_9 \\\n", + "0 51 282 71 92 \n", + "1 51 282 71 92 \n", + "2 51 282 71 92 \n", + "3 51 282 71 92 \n", + "4 51 282 71 92 \n", + "5 51 282 71 92 \n", + "6 51 282 71 92 \n", + "7 51 282 71 92 \n", + "8 51 282 71 92 \n", + "9 51 282 71 92 \n", + "10 51 282 71 92 \n", + "11 51 282 71 92 \n", + "12 51 282 71 92 \n", + "13 51 282 71 92 \n", + "\n", + " sample_test_9 sample_train_10 sample_test_10 sample_train_11 \\\n", + "0 77 148 102 211 \n", + "1 77 148 102 211 \n", + "2 77 148 102 211 \n", + "3 77 148 102 211 \n", + "4 77 148 102 211 \n", + "5 77 148 102 211 \n", + "6 77 148 102 211 \n", + "7 77 148 102 211 \n", + "8 77 148 102 211 \n", + "9 77 148 102 211 \n", + "10 77 148 102 211 \n", + "11 77 148 102 211 \n", + "12 77 148 102 211 \n", + "13 77 148 102 211 \n", + "\n", + " sample_test_11 sample_train_12 sample_test_12 sample_train_13 \\\n", + "0 80 60 76 218 \n", + "1 80 60 76 218 \n", + "2 80 60 76 218 \n", + "3 80 60 76 218 \n", + "4 80 60 76 218 \n", + "5 80 60 76 218 \n", + "6 80 60 76 218 \n", + "7 80 60 76 218 \n", + "8 80 60 76 218 \n", + "9 80 60 76 218 \n", + "10 80 60 76 218 \n", + "11 80 60 76 218 \n", + "12 80 60 76 218 \n", + "13 80 60 76 218 \n", + "\n", + " sample_test_13 sample_train_14 sample_test_14 sample_train_15 \\\n", + "0 142 262 127 46 \n", + "1 142 262 127 46 \n", + "2 142 262 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RF_Regressor_test_subset_MSE_after_ablation_7 \\\n", + "0 5854.865348 \n", + "1 3773.388434 \n", + "2 3773.388434 \n", + "3 3886.957778 \n", + "4 3886.957778 \n", + "5 5830.906037 \n", + "6 5760.281610 \n", + "7 5311.720295 \n", + "8 3579.523161 \n", + "9 3579.523161 \n", + "10 3411.978401 \n", + "11 3411.978401 \n", + "12 5341.277991 \n", + "13 5471.686247 \n", + "\n", + " RF_Regressor_test_subset_R_2_after_ablation_7 \\\n", + "0 -0.029022 \n", + "1 0.336808 \n", + "2 0.336808 \n", + "3 0.316848 \n", + "4 0.316848 \n", + "5 -0.024811 \n", + "6 -0.012398 \n", + "7 0.038456 \n", + "8 0.352024 \n", + "9 0.352024 \n", + "10 0.382353 \n", + "11 0.382353 \n", + "12 0.033105 \n", + "13 0.009498 \n", + "\n", + " RF_Regressor_test_subset_MSE_after_ablation_8 \\\n", + "0 5722.936199 \n", + "1 3983.541473 \n", + "2 3983.541473 \n", + "3 4009.534694 \n", + "4 4009.534694 \n", + "5 5751.261795 \n", + "6 5843.946484 \n", + "7 5419.271537 \n", + "8 3764.500724 \n", + "9 3764.500724 \n", + "10 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\n", + "7 NaN \n", + "8 0.429650 \n", + "9 0.429650 \n", + "10 0.421236 \n", + "11 0.421236 \n", + "12 NaN \n", + "13 -0.054183 \n", + "\n", + " RF_Plus_Regressor_test_MSE_after_ablation_8 \\\n", + "0 NaN \n", + "1 3515.520108 \n", + "2 3515.520108 \n", + "3 3436.433191 \n", + "4 3436.433191 \n", + "5 NaN \n", + "6 5397.205079 \n", + "7 NaN \n", + "8 3374.216506 \n", + "9 3374.216506 \n", + "10 3443.107371 \n", + "11 3443.107371 \n", + "12 NaN \n", + "13 6257.734088 \n", + "\n", + " RF_Plus_Regressor_test_R_2_after_ablation_8 \\\n", + "0 NaN \n", + "1 0.298043 \n", + "2 0.298043 \n", + "3 0.313835 \n", + "4 0.313835 \n", + "5 NaN \n", + "6 -0.077680 \n", + "7 NaN \n", + "8 0.426972 \n", + "9 0.426972 \n", + "10 0.415273 \n", + "11 0.415273 \n", + "12 NaN \n", + "13 -0.062723 \n", + "\n", + " RF_Plus_Regressor_test_MSE_after_ablation_9 \\\n", + "0 NaN \n", + "1 3320.013305 \n", + "2 3320.013305 \n", + "3 3436.462807 \n", + "4 3436.462807 \n", + "5 NaN \n", + "6 5239.421246 \n", + "7 NaN \n", + "8 3804.723494 \n", + "9 3804.723494 \n", + "10 4046.739770 \n", + "11 4046.739770 \n", + "12 NaN \n", + "13 6050.221036 \n", + "\n", + " RF_Plus_Regressor_test_R_2_after_ablation_9 \\\n", + "0 NaN \n", + "1 0.337081 \n", + "2 0.337081 \n", + "3 0.313829 \n", + "4 0.313829 \n", + "5 NaN \n", + "6 -0.046175 \n", + "7 NaN \n", + "8 0.353861 \n", + "9 0.353861 \n", + "10 0.312761 \n", + "11 0.312761 \n", + "12 NaN \n", + "13 -0.027482 \n", + "\n", + " RF_Plus_Regressor_test_MSE_after_ablation_10 \\\n", + "0 NaN \n", + "1 5066.139070 \n", + "2 5066.139070 \n", + "3 5066.139070 \n", + "4 5066.139070 \n", + "5 NaN \n", + "6 5066.139070 \n", + "7 NaN \n", + "8 6013.940639 \n", + "9 6013.940639 \n", + "10 6013.940639 \n", + "11 6013.940639 \n", + "12 NaN \n", + "13 6013.940639 \n", + "\n", + " RF_Plus_Regressor_test_R_2_after_ablation_10 test_data_ablation_time \\\n", + "0 NaN NaN \n", + "1 -0.011575 3.175685 \n", + "2 -0.011575 3.190413 \n", + "3 -0.011575 3.190980 \n", + "4 -0.011575 3.194187 \n", + "5 NaN NaN \n", + "6 -0.011575 3.174683 \n", + "7 NaN NaN \n", + "8 -0.021320 3.235980 \n", + "9 -0.021320 3.261289 \n", + "10 -0.021320 3.266821 \n", + "11 -0.021320 3.251678 \n", + "12 NaN NaN \n", + "13 -0.021320 3.274135 \n", + "\n", + " split_seed \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 \n", + "5 1 \n", + "6 1 \n", + "7 2 \n", + "8 2 \n", + "9 2 \n", + "10 2 \n", + "11 2 \n", + "12 2 \n", + "13 2 " ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -2576,15 +15163,14 @@ "name": "stdout", "output_type": "stream", "text": [ - " fi fi_time\n", - "0 Kernel_SHAP_RF_plus 53.592039\n", - "1 LFI_with_raw_CV_RF 70.349461\n", - "2 LFI_with_raw_OOB_RF 2.535222\n", - "3 LFI_with_raw_RF 3.045225\n", - "4 LFI_with_raw_RF_plus 0.706900\n", - "5 LIME_RF_plus 124.873722\n", - "6 MDI_RF 1.098270\n", - "7 TreeSHAP_RF 0.108346\n" + " fi fi_time\n", + "0 Kernel_SHAP_RF_plus 1.107537e+02\n", + "1 LFI_evaluate_on_all_RF_plus 8.741572e-01\n", + "2 LFI_evaluate_on_oob_RF_plus 9.727947e-01\n", + "3 LFI_fit_on_OOB_RF 7.152557e-07\n", + "4 LFI_fit_on_inbag_RF 1.557891e+00\n", + "5 LIME_RF_plus 1.272089e+02\n", + "6 TreeSHAP_RF 1.609519e-01\n" ] } ], @@ -2600,37 +15186,36 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " fi ablation_time\n", - "0 Kernel_SHAP_RF_plus 37.999997\n", - "1 LFI_with_raw_CV_RF 1.801137\n", - "2 LFI_with_raw_OOB_RF 1.750226\n", - "3 LFI_with_raw_RF 1.777164\n", - "4 LFI_with_raw_RF_plus 38.591149\n", - "5 LIME_RF_plus 37.662260\n", - "6 MDI_RF 1.775139\n", - "7 TreeSHAP_RF 1.757286\n" + "ename": "KeyError", + "evalue": "'Column not found: ablation_time'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# Print the ablation time\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m averages \u001b[39m=\u001b[39m combined_df\u001b[39m.\u001b[39;49mgroupby(\u001b[39m'\u001b[39;49m\u001b[39mfi\u001b[39;49m\u001b[39m'\u001b[39;49m)[\u001b[39m'\u001b[39;49m\u001b[39mablation_time\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39mmean()\u001b[39m.\u001b[39mreset_index()\n\u001b[1;32m 3\u001b[0m \u001b[39mprint\u001b[39m(averages)\n", + "File \u001b[0;32m/usr/local/linux/mambaforge-3.11/lib/python3.11/site-packages/pandas/core/groupby/generic.py:1415\u001b[0m, in \u001b[0;36mDataFrameGroupBy.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1406\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, \u001b[39mtuple\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(key) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 1407\u001b[0m \u001b[39m# if len == 1, then it becomes a SeriesGroupBy and this is actually\u001b[39;00m\n\u001b[1;32m 1408\u001b[0m \u001b[39m# valid syntax, so don't raise warning\u001b[39;00m\n\u001b[1;32m 1409\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m 1410\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mIndexing with multiple keys (implicitly converted to a tuple \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1411\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mof keys) will be deprecated, use a list instead.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 1412\u001b[0m \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[1;32m 1413\u001b[0m stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 1414\u001b[0m )\n\u001b[0;32m-> 1415\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(key)\n", + "File \u001b[0;32m/usr/local/linux/mambaforge-3.11/lib/python3.11/site-packages/pandas/core/base.py:248\u001b[0m, in \u001b[0;36mSelectionMixin.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 247\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj:\n\u001b[0;32m--> 248\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mColumn not found: \u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 249\u001b[0m subset \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj[key]\n\u001b[1;32m 250\u001b[0m ndim \u001b[39m=\u001b[39m subset\u001b[39m.\u001b[39mndim\n", + "\u001b[0;31mKeyError\u001b[0m: 'Column not found: ablation_time'" ] } ], "source": [ - "# Print the ablation time\n", - "averages = combined_df.groupby('fi')['ablation_time'].mean().reset_index()\n", - "print(averages)" + "# # Print the ablation time\n", + "# averages = combined_df.groupby('fi')['ablation_time'].mean().reset_index()\n", + "# print(averages)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#################### Change the following according to the dataset ####################\n", "task = \"regression\" #\"regression\" \"classification\"\n", "########################################################################################\n", - "methods_rf = [\"LFI_with_raw_RF\", \"LFI_with_raw_CV_RF\", \"LFI_with_raw_OOB_RF\", \"MDI_RF\", \"TreeSHAP_RF\"]\n", + "methods_rf = [\"TreeSHAP_RF\", \"LFI_fit_on_inbag_RF\", \"LFI_fit_on_OOB_RF\", \"LFI_evaluate_on_all_RF_plus\", \"LFI_evaluate_on_oob_RF_plus\",\n", + " \"Kernel_SHAP_RF_plus\", \"LIME_RF_plus\"]\n", "methods_rf_plus = [\"Kernel_SHAP_RF_plus\",\"LFI_with_raw_RF_plus\", \"LIME_RF_plus\"]\n", "n_testsize = combined_df[['train_size', 'test_size']].drop_duplicates()\n", "num_features = combined_df['num_features'].drop_duplicates()[0]\n", @@ -2639,7 +15224,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2677,7 +15262,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2725,7 +15310,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { diff --git a/feature_importance/scripts/competing_methods_local.py b/feature_importance/scripts/competing_methods_local.py new file mode 100644 index 0000000..fe5f8d0 --- /dev/null +++ b/feature_importance/scripts/competing_methods_local.py @@ -0,0 +1,551 @@ +import os +import sys +import pandas as pd +import numpy as np +import sklearn.base +from sklearn.base import RegressorMixin, ClassifierMixin +from sklearn.metrics import mean_squared_error +from functools import reduce + +import shap +import lime +import lime.lime_tabular +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import * +from sklearn.metrics import r2_score, mean_absolute_error, accuracy_score, roc_auc_score, mean_squared_error + + +# Feature Importance Methods for RF +def tree_shap_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, X_test_subset, fit): + """ + Compute average treeshap value across observations. + Larger absolute values indicate more important features. + :param X: design matrix + :param y: response + :param fit: fitted model of interest (tree-based) + :return: dataframe of shape: (n_samples, n_features) + """ + def add_abs(a, b): + return abs(a) + abs(b) + + subsets = [(X_train_subset, y_train_subset), (X_test, None), (X_test_subset, None)] + result_tables = [] + + explainer = shap.TreeExplainer(fit) + + for X_data, _ in subsets: + shap_values = explainer.shap_values(X_data, check_additivity=False) + if sklearn.base.is_classifier(fit): + # Shape values are returned as a list of arrays, one for each class + results = np.sum(np.abs(shap_values), axis=-1) + else: + results = np.abs(shap_values) + + result_tables.append(results) + + return tuple(result_tables) + +def LFI_evaluation_RF_MDI(X_train, y_train, X_train_subset, y_train_subset, X_test, X_test_subset, fit, **kwargs): + if isinstance(fit, RegressorMixin): + RFPlus = RandomForestPlusRegressor + elif isinstance(fit, ClassifierMixin): + RFPlus = RandomForestPlusClassifier + else: + raise ValueError("Unknown task.") + + rf_plus_model = RFPlus(rf_model=fit, **kwargs) + rf_plus_model.fit(X_train, y_train) + + subsets = [(X_train, y_train), (X_test, None), (X_test_subset, None)] + result_tables = [] + + for X_data, y_data in subsets: + if np.array_equal(X_data, X_train): + rf_plus_mdi = RFPlusMDI(rf_plus_model, evaluate_on="inbag") + else: + rf_plus_mdi = RFPlusMDI(rf_plus_model, evaluate_on="all") + num_samples, num_features = X_data.shape + local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) + abs_local_feature_importances = np.abs(local_feature_importances) + abs_partial_preds = np.abs(partial_preds) + result_tables.append(abs_local_feature_importances) + result_tables.append(abs_partial_preds) + return tuple(result_tables) + +def LFI_evaluation_RF_OOB(X_train, y_train, X_train_subset, y_train_subset, X_test, X_test_subset, fit, **kwargs): + if isinstance(fit, RegressorMixin): + RFPlus = RandomForestPlusRegressor + elif isinstance(fit, ClassifierMixin): + RFPlus = RandomForestPlusClassifier + else: + raise ValueError("Unknown task.") + + rf_plus_model = RFPlus(rf_model=fit, **kwargs) + rf_plus_model.fit(X_train, y_train) + + subsets = [(X_train, y_train), (X_test, None), (X_test_subset, None)] + result_tables = [] + + for X_data, y_data in subsets: + if np.array_equal(X_data, X_train): + rf_plus_mdi = AloRFPlusMDI(rf_plus_model, evaluate_on="oob") + else: + rf_plus_mdi = AloRFPlusMDI(rf_plus_model, evaluate_on="all") + num_samples, num_features = X_data.shape + local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) + abs_local_feature_importances = np.abs(local_feature_importances) + abs_partial_preds = np.abs(partial_preds) + result_tables.append(abs_local_feature_importances) + result_tables.append(abs_partial_preds) + return tuple(result_tables) + + +# Feature Importance Methods for RF+ +def LFI_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, X_test_subset, fit): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + subsets = [(X_train, y_train), (X_test, None), (X_test_subset, None)] + result_tables = [] + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + + for X_data, y_data in subsets: + num_samples, num_features = X_data.shape + local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) + abs_local_feature_importances = np.abs(local_feature_importances) + abs_partial_preds = np.abs(partial_preds) + result_tables.append(abs_local_feature_importances) + result_tables.append(abs_partial_preds) + + return tuple(result_tables) + +def LFI_evaluation_RF_plus_OOB(X_train, y_train, X_train_subset, y_train_subset, X_test, X_test_subset, fit): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + subsets = [(X_train, y_train), (X_test, None), (X_test_subset, None)] + result_tables = [] + + for X_data, y_data in subsets: + num_samples, num_features = X_data.shape + if np.array_equal(X_data, X_train): + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") + else: + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) + abs_local_feature_importances = np.abs(local_feature_importances) + abs_partial_preds = np.abs(partial_preds) + result_tables.append(abs_local_feature_importances) + result_tables.append(abs_partial_preds) + + return tuple(result_tables) + +def lime_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, X_test_subset, fit): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + subsets = [(X_train_subset, y_train_subset), (X_test_subset, None)] + result_tables = [] + + for X_data, _ in subsets: + num_samples, num_features = X_data.shape + rf_plus_lime = RFPlusLime(fit) + lime_values = rf_plus_lime.explain(X_train=X_train, X_test=X_data) + lime_scores = np.abs(lime_values) + result_tables.append(lime_scores) + + result_table_train_subset, result_table_test_subset = result_tables + + return result_table_train_subset, None, result_table_test_subset + + +def kernel_shap_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, X_test_subset, fit): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + subsets = [(X_train_subset, y_train_subset), (X_test_subset, None)] + result_tables = [] + + for X_data, _ in subsets: + num_samples, num_features = X_data.shape + rf_plus_kernel_shap = RFPlusKernelSHAP(fit) + kernel_shap_scores = rf_plus_kernel_shap.explain(X_train=X_train, X_test=X_data) + kernel_shap_scores = np.abs(kernel_shap_scores) + result_tables.append(kernel_shap_scores) + + result_table_train_subset, result_table_test_subset = result_tables + + return result_table_train_subset, None, result_table_test_subset + + +# result_table = pd.DataFrame(kernel_shap_scores, columns=[f'Feature_{i}' for i in range(num_features)]) +# result_tables.append(result_table) + +# def MDI_local_sub_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Compute local MDI importance for each feature and sample. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "sub", lfi=False)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def MDI_local_all_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Wrapper around MDI+ object to get feature importance scores + +# :param X: ndarray of shape (n_samples, n_features) +# The covariate matrix. If a pd.DataFrame object is supplied, then +# the column names are used in the output +# :param y: ndarray of shape (n_samples, n_targets) +# The observed responses. +# :param rf_model: scikit-learn random forest object or None +# The RF model to be used for interpretation. If None, then a new +# RandomForestRegressor or RandomForestClassifier is instantiated. +# :param kwargs: additional arguments to pass to +# RandomForestPlusRegressor or RandomForestPlusClassifier class. +# :return: dataframe - [Var, Importance] +# Var: variable name +# Importance: MDI+ score +# """ +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "all", lfi=False)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + + +# def LFI_absolute_sum(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="outside")["lfi"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def lime_local(X, y, fit): +# """ +# Compute LIME local importance for each feature and sample. +# Larger values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ + +# np.random.seed(1) +# num_samples, num_features = X.shape +# result = np.zeros((num_samples, num_features)) +# explainer = lime.lime_tabular.LimeTabularExplainer(X, verbose=False, mode='regression') +# for i in range(num_samples): +# exp = explainer.explain_instance(X[i], fit.predict, num_features=num_features) +# original_feature_importance = exp.as_map()[1] +# sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) +# for j in range(num_features): +# result[i,j] = abs(sorted_feature_importance[j][1]) +# # Convert the array to a DataFrame +# result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def tree_shap_local(X, y, fit): +# """ +# Compute average treeshap value across observations. +# Larger absolute values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) +# """ +# explainer = shap.TreeExplainer(fit) +# shap_values = explainer.shap_values(X, check_additivity=False) +# if sklearn.base.is_classifier(fit): +# # Shape values are returned as a list of arrays, one for each class +# def add_abs(a, b): +# return abs(a) + abs(b) +# results = np.sum(np.abs(shap_values),axis=-1) +# else: +# results = abs(shap_values) +# result_table = pd.DataFrame(results, columns=[f'Feature_{i}' for i in range(X.shape[1])]) + +# return result_table + +# def permutation_local(X, y, fit, num_permutations=100): +# """ +# Compute local permutation importance for each feature and sample. +# Larger values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :num_permutations: Number of permutations for each feature (default is 100) +# :return: dataframe of shape: (n_samples, n_features) +# """ + +# # Get the number of samples and features +# num_samples, num_features = X.shape + +# # Initialize array to store local permutation importance +# lpi = np.zeros((num_samples, num_features)) + +# # For each feature +# for k in range(num_features): +# # Permute X_k num_permutations times +# for b in range(num_permutations): +# X_permuted = X.copy() +# X_permuted[:, k] = np.random.permutation(X[:, k]) + +# # Feed permuted data through the fitted model +# y_pred_permuted = fit.predict(X_permuted) + +# # Calculate MSE for each sample +# for i in range(num_samples): +# lpi[i, k] += (y[i]-y_pred_permuted[i])**2 + +# lpi /= num_permutations + +# # Convert the array to a DataFrame +# result_table = pd.DataFrame(lpi, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + + +# def MDI_local_sub_stumps_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Compute local MDI importance for each feature and sample. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, local_scoring_fns=scoring_fns, version = "sub", lfi=False, sample_split=None)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def lime_local(X_train, y_train, X_test, y_test, fit): +# """ +# Compute LIME local importance for each feature and sample. +# Larger values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ +# if isinstance(fit, RegressorMixin): +# mode='regression' +# elif isinstance(fit, ClassifierMixin): +# mode='classification' +# np.random.seed(1) +# num_samples, num_features = X_test.shape +# result = np.zeros((num_samples, num_features)) +# explainer = lime.lime_tabular.LimeTabularExplainer(X_train, verbose=False, mode=mode) +# for i in range(num_samples): +# exp = explainer.explain_instance(X_test[i], fit.predict, num_features=num_features) +# original_feature_importance = exp.as_map()[1] +# sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) +# for j in range(num_features): +# result[i,j] = abs(sorted_feature_importance[j][1]) +# # Convert the array to a DataFrame +# result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def MDI_local_all_stumps_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Wrapper around MDI+ object to get feature importance scores + +# :param X: ndarray of shape (n_samples, n_features) +# The covariate matrix. If a pd.DataFrame object is supplied, then +# the column names are used in the output +# :param y: ndarray of shape (n_samples, n_targets) +# The observed responses. +# :param rf_model: scikit-learn random forest object or None +# The RF model to be used for interpretation. If None, then a new +# RandomForestRegressor or RandomForestClassifier is instantiated. +# :param kwargs: additional arguments to pass to +# RandomForestPlusRegressor or RandomForestPlusClassifier class. +# :return: dataframe - [Var, Importance] +# Var: variable name +# Importance: MDI+ score +# """ +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, local_scoring_fns=scoring_fns, version = "all", lfi=False, sample_split=None)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def LFI_ablation_test_evaluation(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, lfi=True, lfi_abs="none", sample_split=None, train_or_test = "test")["lfi"].values +# mdi_plus_scores = np.abs(mdi_plus_scores) +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +######################## Considering not using these methods +# def LFI_sum_absolute_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, lfi=True, lfi_abs="inside", sample_split=None)["lfi"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def LFI_sum_absolute(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="inside")["lfi"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table \ No newline at end of file